Mobility Matters
Home›Model

Mobile robots in the plane

Anonymous point robots in the Euclidean plane repeat Look–Compute–Move cycles; what they agree on, remember, see and rely on is fixed by its assumption axes.

What is optimised

The primary question has been solvability under the fewest assumptions: which combinations of synchrony, agreement on coordinates, rigidity, multiplicity detection, visibility and memory make a task possible at all. Time (in rounds or epochs) and the number of light colours became the measures later, once the feasibility frontier was mapped.

There are \(n\) robots, modelled as points in \(\mathbb{R}^2\). They are anonymous (no identifiers), identical (same algorithm) and silent (no messages). Each robot repeats a cycle: Look (take a snapshot of the positions of the robots it can see, in its own local coordinate system), Compute (run the algorithm on the snapshot) and Move (move towards the computed point). Unless assumed otherwise robots are oblivious: nothing survives from one cycle to the next.

A scheduler decides when robots are active: in FSYNC all robots act in every round; in SSYNC an arbitrary non-empty subset acts in each round, atomically; in ASYNC cycles are arbitrary and a robot may look at others while they move. Time is counted in rounds (FSYNC, SSYNC) or epochs (ASYNC): intervals of time in which every robot completes at least one full cycle, not necessarily the shortest ones.

This is the OBLOT model of Suzuki and Yamashita and of Flocchini, Prencipe and Santoro; lights are the robots' memory: a light with a constant number of colours, set by its robot, persists between cycles. A robot may see only its own light (FSTA: finite state), only the others' lights (FCOM: finite communication), or all of them (LUMI); oblivious robots (OBLOT) have none. The number of colours is a measure.

How the axes work. As for graphs, level 0 on every axis assumes nothing, and higher levels are stronger assumptions. A problem variant is a point on all axes; the catalogue arranges variants as a tree rooted at no assumptions and as a graph of strengthenings.

How the assumptions fit together

Weak robots and the least that suffices

Suzuki and Yamashita Suzuki & Yamashita 1999 modelled a swarm of identical robots that observe each other's positions at unpredictable times and "communicate" only by their moves, with no common coordinate system. Flocchini, Prencipe and Santoro describe the field that grew from this as asking for "the minimal capabilities that the robots should have in order to solve a problem" Flocchini et al. 2012a, and later work states the goal as identifying "the minimal assumptions on internal capabilities of the robots (e.g., persistent memory, communication) and external conditions of the system (e.g., synchrony, activation scheduler)" Flocchini et al. 2023. The robots are deliberately weak: anonymous, uniform, disoriented, oblivious and silent, the "weakest and de facto standard model" OBLOT Flocchini et al. 2023. Each axis of this model is one capability that can be granted on top of that baseline (memory, agreement, synchrony, detection, sight, identity, randomness), and a result is read as a point in the product of these scales. Impossibilities such as gathering without multiplicity detection Prencipe 2007 tell which capability is needed; algorithms tell which suffices.

The adversary acts in time, in space and in the frames

Correctness is proved against an adversary, and most axes describe what that adversary controls. The scheduler decides when robots act and, in ASYNC, how long each phase lasts, so that a robot may compute on an outdated snapshot Prencipe 2007 Flocchini et al. 2023. Non-rigid moves let it decide where a move ends, subject only to a minimum distance \(\delta\) Prencipe 2007 Flocchini et al. 2016a. Disorientation lets it choose each robot's coordinate system, and in some models change it at every activation Di Luna et al. 2014 Izumi et al. 2012. Faults let it control some robots outright Agmon & Peleg 2006, and with randomization the question is what it may know about the coins Efrima & Peleg 2009. Weakening one of these powers is the usual way to study an axis: SSYNC removes stale snapshots, rigid moves remove the choice of stopping point, a compass removes the choice of frame.

Symmetry is the obstacle

Anonymous, disoriented, deterministic robots in a symmetric configuration receive equivalent snapshots and move symmetrically, so symmetry can persist forever; the formability characterisations are stated in terms of the symmetry of the configuration Suzuki & Yamashita 1999 Dieudonné et al. 2010. Several axes are ways to break it. Agreement on chirality or on axes removes reflections and rotations Dieudonné et al. 2010 Flocchini et al. 2008a; labels distinguish robots Bouzid & Lamani 2011 Asahiro & Yamashita 2025; random bits break ties with probability 1 Dieudonné & Petit 2009 Bramas & Tixeuil 2016; a sequential scheduler activates one robot at a time and so elects a leader for free Flocchini et al. 2026. For gathering, a multiplicity point is itself an asymmetry, which is why multiplicity detection lets \(n \ge 3\) robots gather Cieliebak et al. 2012 while two robots, which cannot create one, need memory, lights or compasses Suzuki & Yamashita 1999 Izumi et al. 2012 Viglietta 2013.

Lights: memory and communication in one register

Persistent memory and explicit communication were both absent from the original model. The light, suggested by Peleg Peleg 2005 and formalised by Das et al. Das et al. 2016, gives each robot a constant number of colours that persist and are seen by all, and it removes the difference between SSYNC and ASYNC Das et al. 2016. Showing only one's own light (FSTA) or only the others' (FCOM) separates remembering from communicating Flocchini et al. 2016a, which turned the memory axis and the scheduler axis into a single landscape of model–scheduler pairs Flocchini et al. 2019b Flocchini et al. 2023. Lights also serve the geometric axes: with obstructed visibility they let robots coordinate without collisions Di Luna et al. 2014, and with two robots they replace persistent memory Viglietta 2013 Heriban et al. 2018.

Where the standard setting sits

The usual setting is weak on the axes that concern computability (anonymous, oblivious, disoriented, deterministic, SSYNC or ASYNC, often non-rigid) and idealised on the geometric ones: point robots, transparent, with unlimited visibility and exact sensing Flocchini et al. 2023 Bandettini et al. 2011. The idealisations were kept because they make the questions about synchrony, memory and agreement clean, and they were later relaxed one at a time: limited visibility Ando et al. 1999 Flocchini et al. 2005, fat bodies Czyzowicz et al. 2009b Agathangelou et al. 2013, opacity Di Luna et al. 2014, faults Agmon & Peleg 2006. In the catalogue every axis starts at its most hostile value, so that the root of each tree is the setting in which nothing is assumed; the standard variant of a problem is then a point well above the root on the geometric axes and close to it on the others.

Notation

Symbols of this model; the notation page has every model and the general conventions.

SymbolMeaningPapers writeUsed in
\(n\)number of robots\(N\) in some mutual visibility paperswhole model
\(f\)maximum number of faulty robots (crashed or Byzantine)whole model
\(\ell\)number of distinct labels of homonymous robots; \(\ell=1\) anonymous, \(\ell=n\) distinct identifiers\(h\)whole model
\(c\)number of colours of a lightwhole model
\(V\)visibility radius (limited visibility)whole model
\(\delta\)minimum distance a robot travels in a non-rigid move before the adversary may stop itwhole model
\(\Delta_0\)diameter of the initial configuration\(D_E\)whole model
\(k\)number of groups (\(k\)-grouping; in partitioning the number of fixed points, each receiving \(n/k\) robots)Bhagat et al. 2021 write \(k\) for the group size of partitioning, which is \(n/k\) herek-grouping, Partitioning, Separation
\(L\)length (circumference) of the ring or circle\(\ell\)Uniform deployment on a ring
\(d\)the target spacing \(L/n\) in uniform deploymentUniform deployment on a ring
\(m\)number of patterns in a sequencePattern sequences
\(\lambda\)contraction factor of a \(\lambda\)-contracting gathering protocolGathering, Near-gathering
\(r, s\)robots (local names)whole model
\(g\)hop diameter of the initial visibility graph\(D_G\), \(D\)Gathering, Scattering
\(\phi\)expected running time function of scattering algorithm \(SA_\phi\)\(f\)Scattering

Assumption axes

Each axis below says where the assumption comes from and why the community studies it. Every axis is a scale. Level 0 assumes nothing; each higher level is a stronger assumption, which makes a problem easier. Values on the same level are alternatives that do not imply each other. An algorithm designed for weaker assumptions works under stronger ones; a lower bound proved under stronger assumptions holds under weaker ones.

Body

0fat robots (unit discs)1point robots

Suzuki and Yamashita's robots are points: each one observes "the positions of all the robots" Suzuki & Yamashita 1999, several robots may share a point, and nobody blocks anybody's view. Czyzowicz, Gąsieniec and Pelc Czyzowicz et al. 2009b introduced fat robots, closed unit discs that cannot overlap and hide what lies behind them: a robot sees another if some segment between their boundary circles meets no third robot, and a robot that touches another stops and ends its cycle. Gathering then asks for "a configuration for which the union of all discs representing them is connected", in which moreover every robot sees all others, so that all know the task is done Czyzowicz et al. 2009b. Their motivation is realism: "In reality, even very small robots occupy some space", so robots may block each other's view and motion Czyzowicz et al. 2009b; later accounts say the same Agathangelou et al. 2013. Czyzowicz et al. 2009b solved three and four oblivious, disoriented robots in ASYNC (non-rigid moves, robots know \(n\)) by case analysis and left \(n \ge 5\) open ("We were unable to do it"); Agathangelou, Georgiou and Mavronicolas Agathangelou et al. 2013 answered it for any \(n\) in ASYNC, assuming chirality and knowledge of \(n\). Fatness is not weaker in every respect: the common radius gives all robots "a common measure of unit" Czyzowicz et al. 2009b, a unit of length "for free" Agathangelou et al. 2013 Bandettini et al. 2011. The catalogue puts fat robots at level 0 because they lose multiplicity points and gain obstruction and collisions; point robots are the setting of almost all other work Bandettini et al. 2011.

Formal definitions
0fat
Each robot is a closed disc of radius 1 (unit disc). Discs may not overlap, so robots cannot share a point and must avoid collisions; a disc hides what lies behind it from other robots. Gathering then asks for a configuration whose union of discs is connected and in which every robot sees all others Czyzowicz et al. 2009b.
1point
Each robot is a point of the plane: several robots may share a point (multiplicity), and a robot hides only what lies exactly behind it on a line of sight.

Scheduler

0asynchronous (ASYNC)1semi-synchronous (SSYNC)2sequential: exactly one robot is activated per round2fully synchronous (FSYNC)

Suzuki and Yamashita Suzuki & Yamashita 1999 introduced the semi-synchronous model: robots act "at infinitely many unpredictable time instants", and in each round of a discrete time an arbitrary non-empty set of robots observes and moves instantaneously, every robot being active infinitely often; Prencipe calls it Atom Prencipe 2007 and FSYNC, where every robot acts in every round, is its special case Das et al. 2016. The asynchronous model (later named CORDA) is due to Flocchini, Prencipe, Santoro and Widmayer Flocchini et al. 1999, as Flocchini et al. 2023 and Prencipe 2007 also state: they assume "asynchronicity within one step", so that "the actual move of a robot may be based on a situation that lies arbitrarily far in the past", and a robot "may be seen an arbitrary number of times by other robots" while moving; they call this "the most radical deviation" from Suzuki and Yamashita's atomic steps Flocchini et al. 1999. In Prencipe's words, "computations can be made based on obsolete observations" Prencipe 2007. Prencipe motivates delays inside a cycle as a way "to model also different motorial speeds of the robots" Prencipe 2007. The schedulers are interesting because they form a hierarchy: rendezvous of two oblivious robots is solvable in FSYNC and not in SSYNC Suzuki & Yamashita 1999 (Theorem 3.1 and footnote 5), while a problem separating SSYNC from ASYNC for oblivious robots was sought "for more than two decades" and is known only under limited visibility Flocchini et al. 2023 Kirkpatrick et al. 2024. Intermediate schedulers (RSYNC, LC-, CM- and Move-atomic ASYNC) were introduced to locate exactly where the power is lost Flocchini et al. 2023 Buchin et al. 2025. Sequential schedulers, which activate one robot per round, break symmetry by themselves; Flocchini, Navarra, Pattanayak, Piselli and Santoro note that their relationship with FSYNC "has so far been ignored" and show that the two are orthogonal Flocchini et al. 2026.

Formal definitions
0ASYNC
ASYNC: each robot's Look, Compute and Move phases take finite but unbounded, adversarially chosen time; a robot may look while others move, and compute on an outdated snapshot. Time is counted in epochs: an epoch is an interval of time in which every robot completes at least one full Look–Compute–Move cycle; it need not be the shortest such interval. The execution is divided into consecutive epochs, and a bound of \(X\) epochs holds for every such division. In FSYNC and SSYNC time is counted in rounds. Called Corda by Prencipe Prencipe 2007. Intermediate variants restrict interleavings: LC-atomic (no Look by another robot between a robot's Look and its Compute), Move-atomic (no Look during another robot's Move) and CM-atomic Flocchini et al. 2023.
1SSYNC
SSYNC: time is divided into rounds; in each round a non-empty subset of robots, chosen by a fair adversary, executes one atomic Look–Compute–Move cycle. Called Atom by Prencipe Prencipe 2007.
2sequential
Sequential: an SSYNC scheduler that activates exactly one robot per round (fairly). Also called a centralized scheduler Terai et al. 2023; a round-robin scheduler is a fair special case.
2FSYNC
FSYNC: every robot executes one atomic cycle in every round.

Lights (persistent memory)

0oblivious, no light (OBLOT)1internal light only (FSTA): a robot sees its own light, not the others'1external light only (FCOM): a robot sees the others' lights, not its own2full light (LUMI): every robot sees all lights, including its own

Suzuki and Yamashita model a robot as "a mobile processor with infinite memory" and call an algorithm oblivious when the new position depends only on the current observation, noting that "oblivious algorithms are self-stabilizing by definition" Suzuki & Yamashita 1999. Obliviousness then became the default, partly because "any algorithm proved correct in this model is also self-stabilizing" Clement et al. 2010 (see also Défago et al. 2020); oblivious, silent robots communicate only by moving, "reminiscent of bees in a bee dance" Flocchini et al. 1999 Prencipe 2007. Unbounded memory was compared early: two non-oblivious robots can rendezvous in SSYNC and oblivious ones cannot Suzuki & Yamashita 1999 (Theorems 3.1 and 3.3) Izumi et al. 2012. Lights were "initially suggested by Peleg" Das et al. 2016 Di Luna et al. 2014: his survey proposes giving robots "a small amount (say, O(1) bits) of stable memory" and "marking (at least some of) the robots with colors, flags or visible indicator lights" Peleg 2005 (Efrima and Peleg likewise give each robot a two-state light bulb, "in effect amounting to one bit of memory and communication", to partition robots by a randomized algorithm Efrima & Peleg 2007); Das, Flocchini, Prencipe, Santoro and Yamashita Das et al. 2016 turned them into the LUMI model, a constant-size register that is persistent and visible to all, so that a robot can remember and communicate a constant number of bits per cycle. Their main results are that LUMI under ASYNC is equivalent to LUMI under SSYNC and strictly stronger than OBLOT under SSYNC Das et al. 2016. To separate the two roles of a light, Flocchini, Santoro, Viglietta and Yamashita Flocchini et al. 2016a defined FSTA (a robot sees only its own light: constant memory, no communication) and FCOM (it sees only the others' lights: constant communication, no memory), and showed that FSTA robots with rigid moves rendezvous in SSYNC and FCOM robots even in ASYNC. The question "is it better to remember or to communicate" then became a programme comparing the four models under every scheduler Flocchini et al. 2019b Flocchini et al. 2023 Das et al. 2024.

Formal definitions
0oblivious
OBLOT: a robot's computation depends only on the current snapshot; nothing persists across cycles.
1internal light (FSTA)
FSTA: each robot has a light with \(c\) colours that only it can see; it persists between cycles (constant persistent memory, no communication). Also called an internal light.
1external light (FCOM)
FCOM: each robot has a light with \(c\) colours that only the others can see (constant communication, no persistent memory of its own). Also called an external light.
2lights (LUMI)
LUMI: each robot has a light with \(c\) colours, set at the end of Compute, visible to all robots including itself; it is both memory and communication. Also called luminous robots, or a full light.

Agreement on coordinates

0no agreement on local coordinate systems1common chirality (same clockwise orientation)1agreement on one axis (no common chirality)2agreement on both axes (common compass)

Suzuki and Yamashita's robots "initially do not have a common x-y coordinate system", although they share handedness: in every local frame "the positive y-direction is 90 degrees counterclockwise from the positive x-direction" Suzuki & Yamashita 1999; together with anonymity, this disorientation is what keeps symmetric configurations symmetric. Flocchini, Prencipe, Santoro and Widmayer Flocchini et al. 1999 Flocchini et al. 2008a dropped that common handedness and made agreement a parameter, studying "the role of common knowledge" in pattern formation by asynchronous robots: with the directions and orientations of both axes any pattern can be formed; with one axis direction (with or without its orientation) the problem is solvable when \(n\) is odd and in general not when \(n\) is even; with no axis it is unsolvable in general (ISAAC version, proofs omitted) Flocchini et al. 1999. The journal version proves these results for agreement on the direction and orientation of one axis, without common handedness (Theorem 4.1, Theorem 5.1, Corollary 2.1, Theorem 7.1 of Flocchini et al. 2008a). Efrima and Peleg Efrima & Peleg 2009, "elaborating on" that work, list full compass, half compass, direction-only, axes-only and no compass, and note that without a common orientation robots "do not share the notion of 'clockwise'". Common handedness, chirality, became the usual intermediate assumption: Dieudonné, Petit and Villain list sense of direction and chirality among the global properties that decide what a swarm can do, and show that leader election and arbitrary pattern formation are equivalent in ASYNC for \(n \ge 4\) with chirality and \(n \ge 5\) without Dieudonné et al. 2010; removing chirality from pattern formation and gathering is a research line of its own Bramas & Tixeuil 2015 Cicerone et al. 2019. Partial agreement has also been quantified: Izumi et al. give each robot an "unreliable compass" and find the largest deviation that still lets two oblivious robots gather Izumi et al. 2012, and agreement on one axis is used for gathering under limited visibility Poudel & Sharma 2021 Castenow et al. 2022.

Formal definitions
0disoriented
Each robot \(r\) observes in its own coordinate system, obtained from the global one by an unknown similarity (translation, rotation, uniform scaling, and possibly reflection); systems may differ between robots and, in some models, between cycles. Papers that call robots disoriented do not always mean this: Défago and Souissi use the word while assuming chirality Défago & Souissi 2008. Some papers also let the adversary change a robot's rotation, reflection and unit at every Look (variable disorientation) Flocchini et al. 2023 Wada et al. 2026.
1chirality
All local coordinate systems have the same handedness (clockwise orientation): no reflection. Also called handedness. Prencipe uses chirality for agreement on North and East, i.e. a compass Prencipe 2007.
1one axis
All robots agree on the direction and orientation of one axis (e.g. North), but not necessarily on handedness: two robots may disagree on which side is East. This is how the papers use one axis agreement (e.g. robots "agree on only 'left' and 'right'" Bose et al. 2021a; robots "do not necessarily have the same handedness" Pagli et al. 2015). One axis and chirality are alternatives on this axis; together they amount to a common compass.
2compass
All robots agree on the directions and orientations of both axes (a common compass); they may still disagree on origin and unit. Called full compass by Efrima and Peleg, who also use half compass (both axis directions, the positive sense of one axis, no chirality), direction-only and axes-only Efrima & Peleg 2009. Agreement on one axis together with common chirality is equivalent: the orientation of the second axis then follows from handedness.

Movement

0non-rigid: the adversary may stop a move after some minimum distance \(\delta\)1rigid: every move reaches its destination

In CORDA a move may stop before its destination: Prencipe's Assumption A1 gives each robot a constant \(\delta_r > 0\) such that it either reaches its destination or travels at least \(\delta_r\) towards it, so that "it cannot know how far it will go in the current cycle" Prencipe 2007. The physical motivation is "limits to the robot's motorial capabilities" Prencipe 2007; the lower bound \(\delta\) is needed because without it no algorithm could be guaranteed to terminate Bandettini et al. 2011. Non-rigid moves let the adversary act in space as the scheduler acts in time, and rigid moves (every move reaches its destination) are the stronger assumption that many results state explicitly. Flocchini, Santoro, Viglietta and Yamashita Flocchini et al. 2016a first proved their rendezvous algorithms for rigid moves and then showed that "rigidity of the movements can be traded with knowledge of \(\delta\)". Heriban, Défago and Tixeuil Heriban et al. 2018 work explicitly in "the non-rigid ASYNC model", and later landscape papers removed rigidity (and chirality) from the earlier synchronous comparisons of OBLOT, FSTA, FCOM and LUMI, as reported by Das et al. 2024.

Formal definitions
0non-rigid
In a Move phase the adversary may stop a robot before its destination, but only after it has travelled at least \(\delta>0\) (a constant unknown to the robots) or reached the destination.
1rigid
Every Move phase ends at the computed destination.

Multiplicity detection

0none: a point with several robots looks like one robot1weak: a robot sees whether a point holds more than one robot2strong: a robot sees how many robots are at each point

With point robots several robots can share a point, and the snapshot decides whether this is visible. In Prencipe's statement of CORDA robots "can not distinguish whether there is more than one fellow on a given position" Prencipe 2007. Prencipe observed that every known gathering algorithm used some "extra" assumption, mainly that robots "detect whether a given point on the plane is occupied by one or more robots", and proved gathering impossible without such help Prencipe 2007. Cieliebak, Flocchini, Prencipe and Santoro Cieliebak et al. 2012 then showed that with multiplicity detection any \(n > 2\) oblivious, disoriented, asynchronous robots gather; their robots tell "none, one, or more than one" robot at a point, and their Theorem 5.36 covers \(n \ge 5\), the cases \(n = 3, 4\) coming from earlier work of Cieliebak and Prencipe Cieliebak et al. 2012; the idea is to create one multiplicity point and keep it unique Bandettini et al. 2011. Later work distinguishes a weak detector (one robot or more) from a strong one (the exact number) Bouzid et al. 2013 Pattanayak et al. 2019b; the earliest source of this split was not traced. The level matters: Terai, Wada and Katayama note that strong detection alone lets robots without lights gather, so a model with lights must keep colours from revealing multiplicities Terai et al. 2023; under sequential schedulers weak detection is necessary and sufficient for gathering Flocchini et al. 2026.

Formal definitions
0no multiplicity detection
A snapshot is a set of points: a point occupied by several robots looks like a single robot.
1weak multiplicity
A snapshot marks each occupied point as holding one robot or more than one.
2strong multiplicity
A snapshot is a multiset: each occupied point comes with the exact number of robots on it.

Visibility

0limited: robots see only within a fixed radius1unlimited

Ando, Oasa, Suzuki and Yamashita Ando et al. 1999 introduced robots that see only within a fixed radius \(V\); Kirkpatrick et al. call it "the pioneering work on Point Convergence under limited visibility", Kirkpatrick et al. 2024. Each robot moves towards the centre of the smallest enclosing circle of the robots it sees, but only so far that every robot it sees stays within distance \(V\); the proof assumes points, no obstruction, and phases of negligible duration (so the scheduler is SSYNC although the paper calls the robots asynchronous), with each move reaching its computed destination, at most a small constant away Ando et al. 1999. Flocchini, Prencipe, Santoro and Widmayer Flocchini et al. 2005 gathered oblivious robots with limited visibility in ASYNC, with non-rigid moves and without multiplicity detection, when they share "orientation", i.e. agree on the directions and orientations of both axes but not on unit or origin; they conclude that "orientation is at least as powerful as instantaneous movements". Limited visibility makes the model local: the initial visibility graph must be connected Flocchini et al. 2005 (Lemma 4.7), and known algorithms keep every initially visible pair visible Ando et al. 1999 Bandettini et al. 2011 Kirkpatrick et al. 2024. It also became a tool for separation: Katreniak solved convergence with limited visibility under 1-bounded asynchrony, adapting a \(k\)-bounded scheduler from earlier work on flocking Katreniak 2011, and convergence with limited visibility is the known problem that separates SSYNC from ASYNC for oblivious robots Kirkpatrick et al. 2024 Flocchini et al. 2023. Das et al. argue that full visibility "is not practically feasible" and compare OBLOT, FSTA, FCOM and LUMI under both visibilities Das et al. 2024.

Formal definitions
0limited visibility
A robot sees exactly the robots within Euclidean distance \(V\) of its position, for a fixed radius \(V>0\).
1unlimited visibility
A robot sees all robots (subject to opacity).

Opacity

0opaque: collinear robots hide each other1transparent: every robot sees every robot within range

In Suzuki and Yamashita's model and in CORDA robots are transparent: "three or more collinear agents are assumed to be mutually visible" Di Luna et al. 2014. Di Luna, Flocchini, Gan Chaudhuri, Santoro and Viglietta Di Luna et al. 2014 Di Luna et al. 2017 started "the study of computing when visibility is obstructable" for point robots, since "given three collinear agents, the one in the middle blocks the visibility between the other two". Before them obstruction had been studied for fat robots Czyzowicz et al. 2009b Agathangelou et al. 2013 and for robots on a line, according to Di Luna et al. 2014. The basic problem they posed, Complete (Mutual) Visibility, asks the robots to reach a configuration in which all see each other; they solved it in ASYNC without collisions with a constant number of colours Di Luna et al. 2014. Opaque robots with lights then became a line of work on time and colours Vaidyanathan et al. 2015 Sharma et al. 2021a and on pattern formation Bose et al. 2021a, and Feletti, Mambretti, Mereghetti and Palano compare the computational power of transparent and opaque robots directly Feletti et al. 2025.

Formal definitions
0opaque
Robots are points that obstruct sight: \(r\) sees \(s\) iff no third robot lies on the open segment \(\overline{rs}\).
1transparent
Robots do not obstruct each other's view.

Faults

0up to \(f\) Byzantine robots1up to \(f\) robots may crash2no faults

Agmon and Peleg Agmon & Peleg 2006 (SODA 2004) brought crash and Byzantine robots into the Look–Compute–Move model, requiring the algorithm "to successfully gather the nonfaulty robots, independently of the behavior of the faulty ones". They observed that most existing algorithms fail with a single crash, gathered \(n \ge 3\) robots with one crash in SSYNC (Theorem 3.8), proved that three robots cannot gather, or even converge, in SSYNC if one may be Byzantine (Theorem 4.4; hence also in ASYNC, which their abstract states), and gathered \(n \ge 3f+1\) robots with \(f\) Byzantine faults in FSYNC (Theorem 5.10) Agmon & Peleg 2006. Their robots share handedness, see multiplicities (a Look returns a multiset) and make non-rigid moves of at least a fixed distance Agmon & Peleg 2006. The motivation is deployment in adverse environments, where a swarm must "ensure coordination in spite of faulty robots" Clement et al. 2010, and oblivious algorithms are already self-stabilizing, so transient faults come for free and permanent ones are the question Défago et al. 2020. Défago et al. Défago et al. 2020 extended the feasibility study to bounded schedulers and to probabilistic gathering under crash and Byzantine faults. A crashed robot "simply stops moving" and, since correct robots may also stay still, cannot be identified Efrima & Peleg 2009; crash-tolerant gathering of many faults was obtained with strong multiplicity detection Bouzid et al. 2013 and with weak multiplicity detection Pattanayak et al. 2019b.

Formal definitions
0Byzantine
Up to \(f\) robots move arbitrarily (adversarially); with lights, they may also set arbitrary colours.
1crash faults
Up to \(f\) robots stop forever at adversarial times and remain visible at their last position.
2no faults
All robots follow the algorithm forever.

Randomness

0deterministic1randomized

The classical model is deterministic: in CORDA the Compute phase runs "a deterministic algorithm" Prencipe 2007, so a configuration that looks the same to two anonymous, disoriented robots makes them act alike forever. Randomization was introduced to escape such impossibilities: Dieudonné and Petit Dieudonné & Petit 2009 proved that scattering cannot be solved deterministically in SSYNC and gave a randomized algorithm, and Défago et al. Défago et al. 2020 studied deterministic gathering under bounded schedulers and probabilistic gathering, with and without crash and Byzantine faults. Clément et al. note that work around the impossibilities had focused on additional assumptions, "less attention being shown to the use of randomization", and analyse probabilistic gathering and scattering with Markov chains Clement et al. 2010. Yamauchi and Yamashita Yamauchi & Yamashita 2014 formed any pattern in ASYNC without a common coordinate system by randomization, for \(n \ge 5\) robots with common handedness, and patterns without multiplicities; Bramas and Tixeuil then treated randomness as a resource, using one random bit per cycle for pattern formation Bramas & Tixeuil 2016 and proving that \(n \log n\) random bits are necessary to scatter \(n\) robots Bramas & Tixeuil 2017. What the adversary may learn about the coin flips (adaptive or not) is part of the model and is fixed per paper Efrima & Peleg 2009.

Formal definitions
0deterministic
The destination computed in a cycle is a function of the snapshot (and the light, if any).
1randomized
Robots may use random bits in Compute; guarantees hold with probability 1 or in expectation, as stated per result.

Identifiers

0anonymous: all robots are identical1homonymous: robots carry labels, but several may share a label2distinct identifiers (eponymous): robots have pairwise distinct identifiers

Suzuki and Yamashita's robots are anonymous: "they all execute the same algorithm and they cannot be distinguished by their appearances" Suzuki & Yamashita 1999. With disorientation and determinism, anonymity makes symmetry the central obstacle, and the characterisations of solvable instances are stated in terms of the symmetry of the configuration Suzuki & Yamashita 1999 Dieudonné et al. 2010. Das, Flocchini, Santoro and Yamashita Das et al. 2015b compared anonymous and identified (eponymous) robots on forming sequences of patterns and found that anonymous robots form only sequences of equal symmetricity while identified robots form nearly all: in SSYNC with rigid moves, chirality and strong multiplicity detection, anonymous robots form a cyclic sequence exactly when all its patterns have the same symmetricity, a multiple of that of the initial configuration, and robots with distinct visible identifiers form every sequence of patterns of at most \(n\) points (Theorems 1 and 3 of Das et al. 2015b). To measure the gap, Bouzid and Lamani Bouzid & Lamani 2011 imported the homonymous model of Delporte-Gallet et al. Delporte-Gallet et al. 2013, in which \(n\) robots share \(\ell\) labels, into the Suzuki–Yamashita model. Asahiro and Yamashita Asahiro & Yamashita 2025 recast labels as algorithms: robots running one of \(\ell\) algorithms behave like robots with \(\ell\) shared labels, so the minimum algorithm size of a task measures how much identification it needs.

Formal definitions
0anonymous
Robots are indistinguishable and run the same algorithm.
1homonymous
Robots carry labels from a set of size \(\ell\), \(1\le\ell\le n\), which may repeat; equivalently, robots may run one of \(\ell\) algorithms. The minimum \(\ell\) that solves a problem is its minimum algorithm size Asahiro & Yamashita 2025.
2distinct IDs (eponymous)
Distinct identifiers (eponymous robots): labels are pairwise distinct (\(\ell=n\)); whether others can see a robot's label is stated per result.

Which model is stronger

Robot models combine a memory model (OBLOT, FSTA, FCOM, LUMI) with a scheduler. Model \(X\) is stronger than \(Y\) (\(X > Y\)) if every problem solvable in \(Y\) is solvable in \(X\) and some problem, the separator, is solvable in \(X\) but not in \(Y\); \(X \equiv Y\) if they solve the same problems; \(X \perp Y\) if each solves a problem the other cannot. The schedulers between FSYNC and ASYNC are RSYNC (after an optional fully synchronous prefix, consecutive sets of active robots are disjoint), SSYNC, and three restrictions of ASYNC: LC-atomic (no robot looks between another's Look and Compute), CM-atomic and Move-atomic (no robot looks while another moves) Flocchini et al. 2023 Buchin et al. 2025.

Most results assume chirality and rigid moves; separators use small teams (often \(n \le 4\)). Sources: Das et al. 2016 Flocchini et al. 2019b Buchin et al. 2025 Flocchini et al. 2023 Das et al. 2021b Naito et al. 2025 Flocchini et al. 2026 Kirkpatrick et al. 2024 Feletti et al. 2026b. The sequential-scheduler relations of Feletti et al. 2026b assume variable disorientation (no chirality, no common unit) and strong multiplicity detection.

Choose a model in the grid to compare every other model with it. strongerweakerequivalentincomparableunknowndashed: follows from other results
FSYNCRSYNCSSYNCLC-atomicCM-atomicMove-atomicASYNC
LUMIfull light
FCOMexternal light
FSTAinternal light
OBLOToblivious

A scheduler that allows fewer executions gives at least as much power: FSYNC \(\ge\) SSYNC, RSYNC \(\ge\) SSYNC, and SSYNC \(\ge\) each atomic ASYNC \(\ge\) ASYNC. More visible light gives at least as much power under the same scheduler: LUMI \(\ge\) FCOM, FSTA \(\ge\) OBLOT.

All proved relations in this view (93)
ModelModelSeparatorSource
OBLOTFSYNC>OBLOTSSYNCRDVSuzuki & Yamashita 1999 Theorem 3.1 (no oblivious algorithm for two robots) and footnote 5 (midpoint algorithm for synchronized robots); see also Flocchini et al. 2019b Thm 20.4 and Das et al. 2016 Lemma 3.3 also Flocchini et al. 2019b
Suzuki and Yamashita's robots share handedness and each move reaches its computed destination (bounded step length); the impossibility holds a fortiori without these.
LUMIASYNC>OBLOTSSYNCRDVDas et al. 2016 Thm 3.4 (via Lemma 3.3, Thm 3.3)
LUMIASYNC≡LUMISSYNCsimulationDas et al. 2016 Thm 3.6 (via Thm 3.5) also Flocchini et al. 2023
FCOMFSYNC≡LUMIFSYNCsimulationFlocchini et al. 2019b Thm 2 (Lemma 1, simulation LUbyFCinFSY)
FCOMFSYNC>FSTAFSYNC¬ILFlocchini et al. 2019b Thm 6 (Lemmas 4, 5) also Buchin et al. 2025
FSTAFSYNC>OBLOTFSYNCOSPFlocchini et al. 2019b Thm 10 (Lemmas 8, 9)
FCOMSSYNC⊥FSTASSYNC¬IL, TAR(d)Flocchini et al. 2019b Thm 14
FSTASSYNC>OBLOTSSYNCRDVFlocchini et al. 2019b Thm 15
⚠ Uses rendezvous by FSTA robots in SSYNC Flocchini et al. 2016a, which needs each robot's unit of distance to stay fixed.
FCOMSSYNC>OBLOTSSYNCRDVFlocchini et al. 2019b Thm 15
LUMISSYNC>FSTASSYNCOSPFlocchini et al. 2019b Thm 17 (Lemma 16)
LUMISSYNC>FCOMSSYNCOSPFlocchini et al. 2019b Thm 17 (Lemma 16)
LUMIFSYNC>LUMISSYNCCGEFlocchini et al. 2019b Thm 20.1 (Lemma 19)
FSTAFSYNC>FSTASSYNCCGEFlocchini et al. 2019b Thm 20.2
FCOMFSYNC>LUMISSYNCCGEFlocchini et al. 2019b Thm 20.3
OBLOTFSYNC⊥FCOMSSYNCSRO, ¬ILFlocchini et al. 2019b Thm 25 (Lemmas 22, 23)
OBLOTFSYNC⊥FSTASSYNCSRO, TAR(d)Flocchini et al. 2019b Thm 25
LUMISSYNC⊥FSTAFSYNC¬IL, CGEFlocchini et al. 2019b Thm 26
FCOMSSYNC⊥FSTAFSYNC¬IL, CGEFlocchini et al. 2019b Thm 26
LUMISSYNC⊥OBLOTFSYNCOSP, CGE*Flocchini et al. 2019b Thm 28 (answers the Das et al. 2016 question for LUMI^A since LUMI^A ≡ LUMI^S)
FSTAFSYNC>OBLOTSSYNCCGE, RDVFlocchini et al. 2019b Table 3 (Th. 15, 20)
OBLOTRSYNC⊥FCOMSSYNCSRO, RDVBuchin et al. 2025 Thm 3.1
OBLOTRSYNC⊥FSTASSYNCSRO, RDVBuchin et al. 2025 Thm 3.2
FCOMRSYNC>FCOMSSYNCSROBuchin et al. 2025 Thm 3.3
FSTARSYNC>FSTASSYNCSROBuchin et al. 2025 Thm 3.4
OBLOTRSYNC>OBLOTSSYNCSROBuchin et al. 2025 Thm 3.5
LUMISSYNC>FSTARSYNCCYCBuchin et al. 2025 Thm 4.1
FSTARSYNC⊥FCOMSSYNCSRO, CYCBuchin et al. 2025 Thm 4.2
LUMISSYNC≡LUMIRSYNCsimulationBuchin et al. 2025 Thm 6 (Thm 5, simulation sim-RS-by-S)
FSTAFSYNC⊥LUMIRSYNCCYC, CGEBuchin et al. 2025 Thm 8.1
FSTAFSYNC⊥FCOMRSYNCCYC, CGEBuchin et al. 2025 Thm 8.2
OBLOTFSYNC⊥LUMIRSYNCOSP, CGE*Buchin et al. 2025 Thm 9.1
OBLOTFSYNC⊥FCOMRSYNCOSP, CGE*Buchin et al. 2025 Thm 9.2
OBLOTFSYNC⊥FSTARSYNCOSP, CGE*Buchin et al. 2025 Thm 9.3 also Naito et al. 2025
FCOMRSYNC≡LUMIRSYNCsimulationBuchin et al. 2025 Thm 11 (Thm 10: LUMI^S ⊆ FCOM^RS)
FCOMRSYNC>FSTARSYNCCYCBuchin et al. 2025 Thm 11
FSTARSYNC>OBLOTRSYNCRDVBuchin et al. 2025 Thm 11 (Lemma 1: RDV ∉ OBLOT^RS)
OBLOTSSYNC>OBLOTASYNCMLCvFlocchini et al. 2023 LIPIcs Thm 6 (arXiv Thm 1) also Kirkpatrick et al. 2024
OBLOTMove-atomic>OBLOTASYNCTFFlocchini et al. 2023 LIPIcs Thm 10 (arXiv Thm 2)
OBLOTSSYNC>OBLOTMove-atomicMLCvFlocchini et al. 2023 LIPIcs Thm 11 (arXiv Thm 3)
OBLOTLC-atomic≡OBLOTASYNCsimulationFlocchini et al. 2023 LIPIcs Thm 11 (arXiv Thm 3)
FCOMSSYNC>FCOMASYNCMLCvFlocchini et al. 2023 LIPIcs Thm 14 (arXiv Thm 4) also Naito et al. 2025
FCOMCM-atomic≡FCOMASYNCsimulationFlocchini et al. 2023 LIPIcs Thm 15 (arXiv Thm 5), simulation SIM
FCOMSSYNC≡FCOMLC-atomicsimulationFlocchini et al. 2023 LIPIcs Thm 17 (arXiv Thm 6)
FCOMLC-atomic>FCOMCM-atomicMLCvFlocchini et al. 2023 LIPIcs Thm 18 (arXiv Thm 7)
FSTASSYNC>FSTAASYNCMLCvFlocchini et al. 2023 LIPIcs Thm 21 (arXiv Thm 8)
FSTAMove-atomic>FSTAASYNCTFFlocchini et al. 2023 LIPIcs Thm 23 (arXiv Thm 9)
FSTASSYNC>FSTAMove-atomicMLCvFlocchini et al. 2023 LIPIcs Thm 24.2 (arXiv Thm 10.2) also Naito et al. 2025
FSTALC-atomic≡FSTAASYNCsimulationFlocchini et al. 2023 LIPIcs Thm 24.1
FCOMLC-atomic⊥FSTAMove-atomicCYC, GCNCLFlocchini et al. 2023 LIPIcs Thm 28.1 (arXiv Thm 11.1)
FCOMLC-atomic⊥FSTAASYNCCYC, GCNCLFlocchini et al. 2023 LIPIcs Thm 28.2
FCOMLC-atomic>OBLOTSSYNCRDVFlocchini et al. 2023 LIPIcs Thm 28.3
FCOMASYNC⊥FSTAMove-atomicCYC, GCNCLFlocchini et al. 2023 LIPIcs Thm 29.1 (arXiv Thm 12.1)
FCOMASYNC⊥FSTAASYNCCYC, GCNCLFlocchini et al. 2023 LIPIcs Thm 29.2 also Das et al. 2021b
FCOMASYNC⊥OBLOTSSYNCMLCv, CYCFlocchini et al. 2023 LIPIcs Thm 29.3
FCOMASYNC>OBLOTMove-atomicRDVFlocchini et al. 2023 LIPIcs Thm 29.4
⚠ Uses rendezvous by FCOM robots in ASYNC with 12 colours Flocchini et al. 2016a, which needs a fixed unit of distance; under variable disorientation it is impossible Wada et al. 2026.
FSTAMove-atomic⊥OBLOTSSYNCRDV, MLCvFlocchini et al. 2023 LIPIcs Thm 30.1 (arXiv Thm 13.1)
⚠ Uses rendezvous by FSTA robots in ASYNC, stated without proof; under variable disorientation it is impossible Wada et al. 2026.
FSTAMove-atomic>OBLOTMove-atomicRDVFlocchini et al. 2023 LIPIcs Thm 30.2 also Naito et al. 2025
⚠ Uses rendezvous by FSTA robots in ASYNC, stated without proof; under variable disorientation it is impossible Wada et al. 2026.
FSTAASYNC⊥OBLOTMove-atomicRDV, TFFlocchini et al. 2023 LIPIcs Thm 30.3
⚠ Uses rendezvous by FSTA robots in ASYNC, stated without proof; under variable disorientation it is impossible Wada et al. 2026.
FSTAASYNC⊥OBLOTSSYNCRDV, MLCvFlocchini et al. 2023 LIPIcs Thm 30.4
⚠ Uses rendezvous by FSTA robots in ASYNC, stated without proof; under variable disorientation it is impossible Wada et al. 2026.
FSTAASYNC>OBLOTASYNCRDVFlocchini et al. 2023 LIPIcs Thm 30.5 also Das et al. 2021b, Naito et al. 2025
⚠ Uses rendezvous by FSTA robots in ASYNC, stated without proof; under variable disorientation it is impossible Wada et al. 2026.
OBLOTFSYNC⊥FSTAASYNCSRO, OCDas et al. 2021b Thm 1 (Lemmas 1, 2)
FCOMASYNC⊥FSTAFSYNC¬IL, CGEDas et al. 2021b Thm 3 (Lemma 3: ¬IL ∈ FCOM^A)
FCOMASYNC⊥FSTASSYNC¬IL, TAR(d)Das et al. 2021b Thm 4
FCOMASYNC⊥OBLOTFSYNC¬IL, SRODas et al. 2021b Thm 5
FCOMSSYNC⊥FSTAASYNC¬IL, IOPDas et al. 2021b Thm 6 (Lemmas 4, 5)
LUMIFSYNC>FSTAFSYNCETENaito et al. 2025 Thm 3 (Lemmas 3–5)
LUMIFSYNC>LUMIRSYNCETENaito et al. 2025 Thm 3
FSTAFSYNC>FSTARSYNCHETNaito et al. 2025 Thm 4 (Lemmas 6–10)
LUMIRSYNC>FSTARSYNCHET, TAR(d)*Naito et al. 2025 Thm 4, Thm 5
FSTAMove-atomic⊥FCOMSSYNCVEC, CYCNaito et al. 2025 Thm 7 (+ CYC from Buchin et al. 2025)
FCOMMove-atomic⊥FSTASSYNCLP–Cv, ZCCNaito et al. 2025 Thm 8, Thm 9
OBLOTFSYNC>OBLOTRSYNCLCM–MNaito et al. 2025 Thm 11
FSTAASYNC⊥OBLOTRSYNCLCM–M, SRONaito et al. 2025 Thm 11 (+ SRO from Flocchini et al. 2019b
FCOMSSYNC>FCOMMove-atomicRDVNaito et al. 2025 Table 1 (compiled 'known' relations
FCOMFSYNC>FCOMRSYNCCGEBuchin et al. 2025 Thm 7
OBLOTSEQ⊥OBLOTFSYNCUPF*, Gathering (no multiplicity detection)Flocchini et al. 2026 Thms 1, 2, 3, 4
OBLOTSEQ>OBLOTSSYNCUPF*Flocchini et al. 2026 Sect. 8.1 (Thm 1 + Thms 2, 3)
OBLOTRROBIN>OBLOTRPERMFanFeletti et al. 2026b Thm 6 (Lemmas 4, 5)
Variable disorientation (no chirality, no common unit) and strong multiplicity detection.
FSTARROBIN>FSTARPERMFanFeletti et al. 2026b Thm 6 (Lemmas 4, 5)
The only relation the paper proves for FSTA under sequential schedulers.
FCOMRROBIN>FCOMRPERMFanFeletti et al. 2026b Thm 6 (Lemmas 4, 5)
LUMIRROBIN>LUMIRPERMFanFeletti et al. 2026b Thm 6 (Lemmas 4, 5)
LUMISEQ≡LUMIRPERMsimulationFeletti et al. 2026b Thm 7 (simulator in LUMI^SEQ of any LUMI^R-PERM algorithm)
LUMISEQ≡LUMIPERMsimulationFeletti et al. 2026b Thm 7 with Thm 1
First shown in the authors' SSS 2025 paper on sequential schedulers (their reference [14]).
LUMISEQ≡LUMIRSEQsimulationFeletti et al. 2026b Thm 7 with Thm 1
OBLOTRPERM>OBLOTPERMSquareCenterFeletti et al. 2026b Thm 11 (Lemmas 9, 10)
⚠ Lemma 10 uses the symmetry of the square together with disorientation: the robot that has just moved takes the same snapshot as the three others.
OBLOTRSEQ>OBLOTSEQSquareCenterFeletti et al. 2026b Thm 11 (Lemmas 9, 10)
OBLOTPERM>OBLOTSEQPinwheelFeletti et al. 2026b Thm 15 (Lemmas 13, 14)
Lemma 13 (Pinwheel in OBLOT^PERM) is taken from the authors' SSS 2025 paper.
OBLOTRPERM>OBLOTRSEQPinwheelFeletti et al. 2026b Thm 15 (Lemmas 13, 14)
OBLOTPERM⊥OBLOTRSEQPinwheel, SquareCenterFeletti et al. 2026b Thm 16
FCOMRPERM≡FCOMRSEQsimulationFeletti et al. 2026b Thm 17 (simulator with a shared parity light)
FCOMPERM>FCOMSEQHalveTwiceFeletti et al. 2026b Thm 21 (Lemmas 19, 20)
⚠ Lemma 20 needs the unit of distance of a robot to change between activations (variable disorientation).
FCOMRPERM>FCOMPERMHalveOnceFeletti et al. 2026b Thm 25 (Lemmas 23, 24)
⚠ Lemma 24 needs the unit of distance of a robot to change between activations (variable disorientation).
FCOMRSEQ>FCOMSEQHalveTwiceFeletti et al. 2026b Table 1 (from Thms 1, 17, 21)
Separating problems (32)
RDV Rendezvous
Two robots must, within finite time, occupy the same point (not fixed in advance) and stay there. Suzuki & Yamashita 1999 Flocchini et al. 2016a
OSP Oscillating Points
Two robots, from any distance d(t0) > 0, alternately get strictly closer (monotonically) and strictly farther (monotonically) forever. Das et al. 2016
¬IL ¬IL
Three robots in a fixed initial pattern must form a second pattern (a moves 90° clockwise) and then an asymmetric third pattern (b moves 45° clockwise) whose formation requires b to tell a from c. Flocchini et al. 2019b
TAR(d) Triangle Rotation
Three robots forming triangle ABC (circumcircle C, d known) must form A'B'C' on the same circumcircle with |AA'| = |BB'| = |CC'| = d. Flocchini et al. 2019b
TAR(d)* Perpetual Triangle Rotation
TAR(d) repeated forever from each new triangle. Naito et al. 2025
CGE Center of Gravity Expansion
Every robot moves directly from (x,y) to (⌊2x − c_x⌋, ⌊2y − c_y⌋), (c_x, c_y) the initial centre of gravity, then stops. Flocchini et al. 2019b
CGE* Perpetual CGE
CGE repeated forever from each new configuration. Flocchini et al. 2019b
SRO Shrinking Rotation
Two robots repeatedly move so that the segment either rotates 90° clockwise about its midpoint or rotates 45° and shrinks by 1/√2, always staying inside the square of the previous step. Flocchini et al. 2019b
CYC Cyclic Circles
n ≥ 3 robots: n−1 on a circle, one at the centre; the centre robot must visit, in cyclic order, the 2^{n−1} positions at distances d(i) (d non-invertible) along a fixed radius, returning to the centre between visits. Buchin et al. 2025
MLCv Monotone Line Convergence
CLCv with the distance between the robots never increasing. Flocchini et al. 2023
MCv Monotone Convergence
Two robots converge to a common point without ever increasing their distance (no line constraint). Kitamura et al. 2025
TF Trapezoid Formation
Four robots on a convex quadrilateral with one longest side CD: stay still if it is a trapezoid; otherwise exactly one of a, b (chosen by whether angle α ≥ π/4) moves perpendicular to CD to make ab ∥ CD. Flocchini et al. 2023
GCNCL Get Closer but Not too Close on Line
Two robots approach each other on their segment, never increasing distance, and stop at final distance in [d0/2, d0). Flocchini et al. 2023
SM Single Move
Each of two robots makes exactly one non-null move (attributed to Flocchini et al. 2023 by Naito et al. 2025 and Kitamura et al. 2025, but not in its arXiv or LIPIcs text). Naito et al. 2025 Kitamura et al. 2025
DMSD Dyadic-Multiple Stopped-Distance
Whenever both of two robots are stopped, their distance is a dyadic-rational multiple of the initial distance. Kitamura et al. 2025
RDAM Rendezvous in one move or Asymmetric Move
RDV1 (meet after exactly one move each) or AM (one robot moves ≤ 1 time and stops, the other moves ≥ 2 times). Kitamura et al. 2025
ETE Exponential Times Expansion
n ≥ 3 robots (circle + centre) must form a sequence of 2^{n−1} expansion patterns determined by a non-invertible function (details in paper). Naito et al. 2025
HET Hexagonal Edge Traversal
Two robots on opposite vertices of a regular hexagon must perform a prescribed cyclic 13-step traversal (edge to adjacent vertex, diagonal, return). Naito et al. 2025
VEC Vertical Configuration
SRO-like two-robot rotation problem with an extra 'stay' option and conditions 1–5 (see paper). Naito et al. 2025
ZCC Zig-Zag Collision-free Convergence
Two robots converge collision-free while oscillating along their line (alternately approaching and receding from the start). Naito et al. 2025
LP–Cv Leave the Place – Convergence
Each robot first moves away from the other along their line, then they converge. Naito et al. 2025
LCM–M Least Common Multiple – Movement
Three collinear robots A, B, C with AB:BC = 2:1; B must twice move away from A by d_AB, C once away from A by d_AC. Naito et al. 2025
OC Oscillating Configurations
Four robots must cycle forever through configurations I-II-III-II-I-… Das et al. 2021b
IOP Independent Oscillating Problem
Three collinear robots; each end robot independently oscillates between distance x_i and 2x_i from the middle robot whenever activated. Das et al. 2021b
EPG* Perpetual Expansion Preserving Gravity
Two robots must forever double the segment joining them while keeping its midpoint at the initial midpoint, read at the times when neither robot moves; replaces CGE* under variable disorientation. Ninomiya et al. 2026
CFSM Collision-Free Single Move
Each of two robots makes exactly one non-null move, and the robots never occupy the same point, even while moving. Ninomiya et al. 2026
VSS Visit Swap and Stop
With \(A = 2a_0 - b_0\) and \(B = 2b_0 - a_0\), every move ends in \(\{A, a_0, b_0, B\}\); the robots first form \((B, b_0)\), \((a_0, A)\) or \((B, A)\) with move counts \((1,0)\), \((0,1)\) or \((1,1)\), then stop forever in one of \((a_0,b_0)\), \((B,b_0)\), \((a_0,A)\), \((B,A)\). Ninomiya et al. 2026
Fan Fan
\(n + 2\) robots on a scalene right triangle, \(n > 3\) of them on the right-angle vertex and one on each other vertex: forever, the robots on the right-angle vertex must move to distinct points and come back, each robot returning every time to the same point; the robots on the other two vertices never move. Feletti et al. 2026b
SquareCenter Square Center
Four robots a, b, c, d on a kite with symmetry axis ad, a right angle at a and an acute angle at d: d moves along the axis to complete a square with a, b, c, then one robot other than d moves to the centre of the square; no other move is allowed. Feletti et al. 2026b
Pinwheel Pinwheel
Two similar acute scalene triangles with the same orientation and circumcentre, the inner one with one robot per vertex, the outer one with \(m \ge 3\) robots in total on its vertices, and \(m + 1\) robots on the circumcentre: forever, the robots on the outer triangle move along its edges to the next vertex in the clockwise order fixed by the inner triangle, without stopping on the way; no other move is allowed. Feletti et al. 2026b
HalveTwice Halve Twice
Three collinear robots, two on one point and the third at distance d0 from it: the third robot must move along the line and stop forever at distance d0/2 or d0/4, stopping at no other distance; no other move is allowed. Feletti et al. 2026b
HalveOnce Halve Once
As HalveTwice, but the third robot must stop forever at distance d0/2, with no other stop. Feletti et al. 2026b

Other settings

Where the papers disagree

Open questions

  1. Is there a problem separating k-bounded asynchrony from SSYNC Kirkpatrick et al. 2024?
  2. The landscape without chirality and rigid moves, especially under ASYNC Flocchini et al. 2019b Kitamura et al. 2025.
  3. Can FSTA robots solve rendezvous in ASYNC with a fixed unit of distance Flocchini et al. 2016a Wada et al. 2026?
  4. Is LUMI equally strong under SSYNC and ASYNC for opaque robots Feletti et al. 2025?
  5. Which model classes have no problem solvable there but in no strictly weaker model (the "white regions") Naito et al. 2025? For two robots with variable disorientation, chirality and rigid moves there are none: every upward-closed set of the 13 classes is the exact solvability set of some task Ninomiya et al. 2026, a preprint (the exactness proofs, its Appendices F and G, were not checked here). The question is open for three or more robots.
  6. Schedulers beyond the atomicity-defined ones, and robots with energy constraints Flocchini et al. 2023 Buchin et al. 2025.
  7. The relations of FSTA under the sequential schedulers other than RROBIN, and the relations across memory models among sequential settings, e.g. FSTA under R-SEQ against OBLOT under R-PERM Feletti et al. 2026b.

Problems in this model

Problem familyFormation problems

Form a geometric pattern from a starting configuration. Gathering (one point, through k-grouping), circle formation and uniform circle formation are special cases; convergence is gathering's relaxation.

Pattern formation

Robots start at distinct points and must form a given geometric pattern, up to similarity; which patterns are formable, and from which initial configurations?
12 variants · 4 open questions

Problem familySymmetry breaking and spreading

Tasks whose difficulty is breaking the symmetry of anonymous robots: electing a leader, scattering from shared points, and separating by colour.

Papers

180 papers are cited in this model, listed under each problem that cites them, oldest first. Filter the references by this model →

Pattern formation 37 papers
  • Flocchini et al. 1999 Hard Tasks for Weak Robots: The Role of Common Knowledge in Pattern Formation by Autonomous Mobile Robots cited in text
  • Suzuki & Yamashita 1999 Distributed anonymous mobile robots: formation of geometric patterns posed the problem
  • Efrima & Peleg 2009 Distributed algorithms for partitioning a swarm of autonomous mobile robots cited in text
  • Flocchini et al. 2008a Arbitrary pattern formation by asynchronous, anonymous, oblivious robots introductory result, current best, lower bound
  • Dieudonné et al. 2010 Leader election problem versus pattern formation problem other result
  • Fujinaga et al. 2015 Pattern formation by oblivious asynchronous mobile robots other result
  • Flocchini et al. 2017 Distributed computing by mobile robots: uniform circle formation cited in text
  • Yamauchi & Yamashita 2014 Randomized Pattern Formation Algorithm for Asynchronous Oblivious Mobile Robots introductory result
  • Bramas & Tixeuil 2016 Probabilistic asynchronous arbitrary pattern formation other result
  • Krupke et al. 2015 A Parallel Distributed Strategy for Arraying a Scattered Robot Swarm further reading
  • Cicerone et al. 2019 Asynchronous arbitrary pattern formation: the effects of a rigorous approach current best
  • Sil & Chaudhuri 2017 preprint Line Formation by Fat Robots under Limited Visibility further reading
  • Tomita et al. 2017 Plane Formation by Synchronous Mobile Robots without Chirality further reading
  • Bose et al. 2020a Arbitrary pattern formation on infinite grid by asynchronous oblivious robots further reading
  • Bramas & Tixeuil 2018 Arbitrary pattern formation with four robots cited in text
  • Coppola et al. 2018 preprint Provable Emergent Pattern Formation by a Swarm of Anonymous, Homogeneous, Non-Communicating, Reactive Robots with Limited Relative Sensing and no Global Knowledge or Positioning further reading
  • Bose et al. 2020b Arbitrary Pattern Formation by Opaque Fat Robots with Lights current best
  • Bose et al. 2021a Arbitrary pattern formation by asynchronous opaque robots with lights current best, lower bound
  • Coppola et al. 2019 Provable self-organizing pattern formation by a swarm of robots with limited knowledge further reading
  • Défago et al. 2023 Using model checking to formally verify rendezvous algorithms for robots with lights in Euclidean space cited in text
  • Ehresmann et al. 2019 Distributed Pattern Formation in a Ring further reading
  • Bose et al. 2021b Pattern Formation by Robots with Inaccurate Movements further reading
  • Cicerone et al. 2021a A structured methodology for designing distributed algorithms for mobile entities other result
  • Cicerone 2021 Breaking Symmetries on Tessellation Graphs via Asynchronous Robots: The Line Formation Problem as a Case Study further reading
  • Cicerone et al. 2023a Arbitrary Pattern Formation on Infinite Regular Tessellation Graphs further reading
  • Coleman et al. 2020 preprint Minimizing The Maximum Distance Traveled To Form Patterns With Systems of Mobile Robots further reading
  • Pattanayak et al. 2020b Conic Formation in Presence of Faulty Robots further reading
  • Cicerone et al. 2021c Solving the Pattern Formation by Mobile Robots With Chirality current best
  • Asahiro et al. 2022 Monotonic self-stabilization and its application to robust and adaptive pattern formation further reading
  • Ghosh et al. 2023a Move optimal and time optimal arbitrary pattern formations by asynchronous robots on infinite grid further reading
  • Ghosh et al. 2023b Oblivious Robots Performing Different Tasks on Grid Without Knowing Their Team Members further reading
  • Kundu et al. 2022 preprint Arbitrary pattern formation by asynchronous opaque robots on infinite grid further reading
  • Alsaedi et al. 2025a Pattern formation for fat robots with memory further reading
  • Alsaedi et al. 2025b Pattern formation for fat robots with lights current best
  • Mondal et al. 2024a Arbitrary pattern formation on a continuous circle by oblivious robot swarm further reading
  • Hahn et al. 2024 Forming large patterns with local robots in the OBLOT model current best
  • Kojima et al. 2026 preprint Hardwired Pattern Formation by Mobile Robots with Common Unit Distance further reading
Gathering 59 papers
  • Suzuki & Yamashita 1999 Distributed anonymous mobile robots: formation of geometric patterns posed the problem, lower bound
  • Flocchini et al. 2005 Gathering of asynchronous robots with limited visibility current best, lower bound
  • Cieliebak et al. 2012 Distributed Computing by Mobile Robots: Gathering current best
  • Prencipe 2007 Impossibility of gathering by a set of autonomous mobile robots lower bound
  • Agmon & Peleg 2006 Fault-Tolerant Gathering Algorithms for Autonomous Mobile Robots introductory result, current best, lower bound
  • Czyzowicz et al. 2009b Gathering few fat mobile robots in the plane current best
  • Défago et al. 2020 Self-stabilizing gathering of mobile robots under crash or Byzantine faults cited in text
  • Delporte-Gallet et al. 2013 Byzantine agreement with homonyms cited in text
  • Chaudhuri & Mukhopadhyaya 2010 Gathering Asynchronous Transparent Fat Robots introductory result
  • Bandettini et al. 2011 preprint A Survey on Open Problems for Mobile Robots cited in text
  • Bouzid & Lamani 2011 Robot Networks with Homonyms: The Case of Patterns Formation cited in text
  • Degener et al. 2011 A tight runtime bound for synchronous gathering of autonomous robots with limited visibility introductory result
  • Izumi et al. 2012 The gathering problem for two oblivious robots with unreliable compasses cited in text
  • Agathangelou et al. 2013 A distributed algorithm for gathering many fat mobile robots in the plane current best
  • Bouzid et al. 2013 Gathering of Mobile Robots Tolerating Multiple Crash Faults other result
  • Das et al. 2016 Autonomous mobile robots with lights introductory result
  • Chaudhuri & Mukhopadhyaya 2015 Leader election and gathering for asynchronous fat robots without common chirality current best
  • Viglietta 2013 Rendezvous of Two Robots with Visible Bits other result, lower bound
  • Flocchini et al. 2016a Rendezvous with constant memory introductory result, current best, lower bound
  • Flocchini et al. 2017 Distributed computing by mobile robots: uniform circle formation cited in text
  • Bhagat et al. 2016a Fault-tolerant gathering of asynchronous oblivious mobile robots under one-axis agreement current best
  • Bramas & Tixeuil 2015 Wait-Free Gathering Without Chirality other result
  • Bramas & Tixeuil 2016 Probabilistic asynchronous arbitrary pattern formation cited in text
  • Courtieu et al. 2016 Certified Universal Gathering in \(\mathbb{R}^2\) for Oblivious Mobile Robots further reading
  • Balabonski et al. 2018b Synchronous Gathering without Multiplicity Detection: a Certified Algorithm current best
  • Pattanayak et al. 2019b Gathering of mobile robots with weak multiplicity detection in presence of crash-faults current best, lower bound
  • Czyzowicz et al. 2020e Gathering in the plane of location-aware robots in the presence of spies further reading
  • Di Luna et al. 2020a Meeting in a polygon by anonymous oblivious robots further reading
  • Heriban et al. 2018 Optimally Gathering Two Robots current best
  • Li et al. 2021a A continuous strategy for collisionless gathering further reading
  • Okumura et al. 2017 Brief Announcement: Optimal Asynchronous Rendezvous for Mobile Robots with Lights cited in text
  • Poudel & Sharma 2021 Time-Optimal Gathering under Limited Visibility with One-Axis Agreement current best, lower bound
  • Okumura et al. 2023 Optimal L-algorithms for rendezvous of asynchronous mobile robots with external-lights lower bound
  • Terai et al. 2023 Gathering problems for autonomous mobile robots with lights introductory result, current best, lower bound
  • Barel et al. 2021 Probabilistic Gathering of Agents with Simple Sensors further reading
  • Défago et al. 2023 Using model checking to formally verify rendezvous algorithms for robots with lights in Euclidean space cited in text
  • Flocchini et al. 2019a Gathering and Election by Mobile Robots in a Continuous Cycle further reading
  • Pattanayak et al. 2021a Randomized gathering of asynchronous mobile robots further reading
  • Bramas et al. 2020a Stand Up Indulgent Rendezvous further reading
  • Braun et al. 2020 Local Gathering of Mobile Robots in Three Dimensions further reading
  • Castenow et al. 2021 Gathering a Euclidean Closed Chain of Robots in Linear Time further reading
  • Cicerone et al. 2021a A structured methodology for designing distributed algorithms for mobile entities cited in text
  • Di Luna et al. 2025c Gathering on a circle with limited visibility by anonymous oblivious robots further reading
  • Bramas et al. 2023b Stand up indulgent gathering lower bound
  • Heriban & Tixeuil 2021 preprint Unreliable Sensors for Reliable Efficient Robots further reading
  • Nakai et al. 2021 Asynchronous Gathering Algorithms for Autonomous Mobile Robots with Lights current best
  • Castenow et al. 2022 A unifying approach to efficient (near)-gathering of disoriented robots with limited visibility current best, lower bound
  • Kim et al. 2022 Gathering of Mobile Robots with Defected Views further reading
  • Asahiro & Yamashita 2025 Minimum algorithm sizes for the gathering and related problems of autonomous mobile robots current best, lower bound
  • Flocchini et al. 2023 On Asynchrony, Memory, and Communication: Separations and Landscapes cited in text
  • Ghosh et al. 2025 Asynchronous Gathering of Finite Memory Robots on a Circle under Limited Visibility further reading
  • Flocchini et al. 2026 Universal pattern formation by oblivious robots under sequential schedulers cited in text
  • Otaka et al. 2026 Gathering semi-synchronously scheduled two-state robots current best, lower bound
  • Bonnet et al. 2026a Deterministic color-optimal self-stabilizing semi-synchronous gathering: Two certified algorithms current best
  • Pramanick et al. 2025 preprint Asynchronous Gathering of Opaque Robots with Mobility Faults cited in text
  • Sharma et al. 2025b Gathering of Asynchronous Robots on Circle with Limited Visibility Using Finite Communication further reading
  • Aponte et al. 2026 Formal Certification of async Protocols: The Case of Gathering in \(\mathbb{R}^2\) Using Weber Points further reading
  • Wada et al. 2026 preprint Rendezvous under Variable Disorientation: The Algorithmic Power of Fixed Unit Distance current best
  • Shukla et al. 2026b preprint Gathering Autonomous Mobile Robots Under the Adversarial Defected View Model further reading
Circle formation 6 papers
  • Défago & Konagaya 2002 Circle formation for oblivious anonymous mobile robots with no common sense of orientation posed the problem, introductory result
  • Défago & Souissi 2008 Non-uniform circle formation algorithm for oblivious mobile robots with convergence toward uniformity current best
  • Flocchini et al. 2017 Distributed computing by mobile robots: uniform circle formation current best
  • Goswami et al. 2024a Circle formation by asynchronous opaque fat robots on an infinite grid further reading
  • Feletti et al. 2026a Optimal luminous UCF under asynchrony cited in text
  • Maiti et al. 2026a preprint Conflicting Pattern Formation by Teams of Anonymous, Fully Disoriented Robots further reading
Uniform circle formation 14 papers
  • Suzuki & Yamashita 1999 Distributed anonymous mobile robots: formation of geometric patterns cited in text
  • Défago & Konagaya 2002 Circle formation for oblivious anonymous mobile robots with no common sense of orientation cited in text
  • Dieudonné et al. 2008 Circle formation of weak mobile robots posed the problem, introductory result
  • Flocchini et al. 2017 Distributed computing by mobile robots: uniform circle formation current best, lower bound
  • Mamino & Viglietta 2016 Square formation by asynchronous oblivious robots other result
  • Mondal & Chaudhuri 2017 preprint Uniform Circle Formation by Transparent Fat Robots current best
  • Feletti et al. 2018 Uniform circle formation for swarms of opaque robots with lights introductory result
  • Mondal et al. 2020 preprint Uniform Circle Formation By Oblivious Swarm Robots current best
  • Feletti et al. 2023b Uniform circle formation for fully, semi-, and asynchronous opaque robots with lights introductory result, current best
  • Biswas et al. 2023 Multiple Uniform Circle Formation by Fat Robots Under Limited Visibility further reading
  • Feletti et al. 2023a \(O(\log n)\)-time uniform circle formation for asynchronous opaque luminous robots other result
  • Feletti et al. 2026a Optimal luminous UCF under asynchrony current best, lower bound
  • Pattanayak & Sharma 2024 Time-color tradeoff on uniform circle formation by asynchronous robots other result
  • Maiti et al. 2026b preprint Min-Max Uniform Circle Formation by Asynchronous Mobile Robots further reading
Convergence 13 papers
  • Ando et al. 1999 Distributed memoryless point convergence algorithm for mobile robots with limited visibility current best
  • Suzuki & Yamashita 1999 Distributed anonymous mobile robots: formation of geometric patterns posed the problem
  • Cohen & Peleg 2005 Convergence properties of the gravitational algorithm in asynchronous robot systems current best
  • Efrima & Peleg 2009 Distributed algorithms for partitioning a swarm of autonomous mobile robots cited in text
  • Cord-Landwehr et al. 2011 A new approach for analyzing convergence algorithms for mobile robots cited in text
  • Izumi et al. 2012 The gathering problem for two oblivious robots with unreliable compasses cited in text
  • Katreniak 2011 Convergence with limited visibility by asynchronous mobile robots introductory result
  • Pagli et al. 2015 Getting close without touching: near-gathering for autonomous mobile robots cited in text
  • Pattanayak et al. 2017 Convergence of Even Simpler Robots without Position Information further reading
  • De Carufel & Flocchini 2017 Fault-Induced Dynamics of Oblivious Robots on a Line further reading
  • Kirkpatrick et al. 2024 On the power of bounded asynchrony: convergence by autonomous robots with limited visibility current best
  • Castenow et al. 2022 A unifying approach to efficient (near)-gathering of disoriented robots with limited visibility cited in text
  • Asahiro & Yamashita 2023 Compatibility of Convergence Algorithms for Autonomous Mobile Robots (Extended Abstract) further reading
Scattering 7 papers
Leader election in the plane 4 papers
  • Dieudonné et al. 2010 Leader election problem versus pattern formation problem cited in text
  • Dieudonné et al. 2013 Deterministic geoleader election in disoriented anonymous systems posed the problem, current best, lower bound
  • Chaudhuri & Mukhopadhyaya 2015 Leader election and gathering for asynchronous fat robots without common chirality current best, lower bound
  • Cicerone et al. 2019 Asynchronous arbitrary pattern formation: the effects of a rigorous approach cited in text
Universal pattern formation 2 papers
  • Flocchini et al. 2026 Universal pattern formation by oblivious robots under sequential schedulers posed the problem, current best, lower bound
  • Feletti et al. 2026c Universal Dancing by Luminous Robots Under Sequential Schedulers cited in text
Pattern sequences 4 papers
  • Das et al. 2015b Forming sequences of geometric patterns with oblivious mobile robots posed the problem, current best
  • Das et al. 2020 Forming Sequences of Patterns With Luminous Robots current best, lower bound
  • Das et al. 2019d Oblivious Permutations on the Plane further reading
  • Feletti et al. 2026c Universal Dancing by Luminous Robots Under Sequential Schedulers current best, lower bound
Near-gathering 6 papers
  • Flocchini et al. 2005 Gathering of asynchronous robots with limited visibility cited in text
  • Pagli et al. 2015 Getting close without touching: near-gathering for autonomous mobile robots posed the problem, current best
  • Di Luna et al. 2021 TuringMobile: a turing machine of oblivious mobile robots with limited visibility and its applications further reading
  • Li et al. 2021a A continuous strategy for collisionless gathering further reading
  • Castenow et al. 2022 A unifying approach to efficient (near)-gathering of disoriented robots with limited visibility current best, lower bound
  • Gerlach et al. 2024 Symmetry Preservation in Swarms of Oblivious Robots with Limited Visibility further reading
Mutual visibility 25 papers
  • Bhagat et al. 2016b Formation of General Position by Asynchronous Mobile Robots Under One-Axis Agreement further reading
  • Di Luna et al. 2014 Robots with Lights: Overcoming Obstructed Visibility Without Colliding cited in text
  • Di Luna et al. 2017 Mutual visibility by luminous robots without collisions posed the problem, introductory result, current best, lower bound
  • Sharma et al. 2015a Bounds on Mutual Visibility Algorithms further reading
  • Sharma et al. 2015b Mutual Visibility with an Optimal Number of Colors current best, other result
  • Vaidyanathan et al. 2015 Logarithmic-Time Complete Visibility for Robots with Lights current best
  • Sharma et al. 2016 Complete Visibility for Robots with Lights in O(1) Time other result
  • Aljohani & Sharma 2018 Complete Visibility for Mobile Robots with Lights Tolerating Faults current best, lower bound
  • Bhagat & Mukhopadhyaya 2019 Mutual Visibility by Robots with Persistent Memory current best
  • Sharma et al. 2017 O(log N)-Time Complete Visibility for Asynchronous Robots with Lights other result
  • Sharma et al. 2021a Constant-Time Complete Visibility for Robots with Lights: The Asynchronous Case current best, lower bound
  • Poudel et al. 2021 Fault-tolerant complete visibility for asynchronous robots with lights under one-axis agreement current best, other result
  • Sharma et al. 2018 The Complete Visibility Problem for Fat Robots with Lights introductory result
  • Dolev et al. 2019 Brief Announcement: Neighborhood Mutual Remainder and Its Self-Stabilizing Implementation of Look-Compute-Move Robots further reading
  • Sharma et al. 2021b Optimal Randomized Complete Visibility on a Grid for Asynchronous Robots with Lights further reading
  • Alsaedi et al. 2023a The Mutual Visibility Problem for Fat Robots current best, lower bound
  • Alsaedi et al. 2023b preprint Shortest Paths of Mutually Visible Robots further reading
  • Cicerone et al. 2023b The geodesic mutual visibility problem: Oblivious robots on grids and trees further reading
  • Cicerone et al. 2023c Time-Optimal Geodesic Mutual Visibility of Robots on Grids Within Minimum Area further reading
  • Cicerone et al. 2025a Mutual and total mutual visibility in hypercube-like graphs further reading
  • Kim et al. 2023 preprint Complete Visibility Algorithm for Autonomous Mobile Luminous Robots under an Asynchronous Scheduler on Grid Plane further reading
  • Kim et al. 2025 Complete Visibility Algorithms of Luminous Robots With Two-Color Lights on Grid further reading
  • Badri et al. 2024 preprint An optimal algorithm for geodesic mutual visibility on hexagonal grids further reading
  • Bhagat et al. 2024 Collision-Free Linear Time Mutual Visibility for Asynchronous Fat Robots current best
  • Shukla et al. 2026a preprint Budget-Constrained Fault-Tolerant Mutual Visibility for Autonomous Robots under the Mobility Fault Model current best
k-grouping 2 papers
  • Efrima & Peleg 2009 Distributed algorithms for partitioning a swarm of autonomous mobile robots posed the problem, current best, lower bound
  • Liu et al. 2018 Team assembling problem for asynchronous heterogeneous mobile robots cited in text
Separation 3 papers
  • Seike & Yamauchi 2023 Separation of Unconscious Colored Robots posed the problem, introductory result
  • Flocchini et al. 2025 Asynchronous Separation of Unconscious Colored Robots current best, lower bound
  • Pyati et al. 2026 Separation of unconscious colored robots with obstructed visibility current best
Flocking 1 paper
  • Gervasi & Prencipe 2004 Coordination without communication: the case of the flocking problem posed the problem, current best
Uniform deployment on a ring 3 papers
  • Cohen & Peleg 2008 Local spreading algorithms for autonomous robot systems cited in text
  • Flocchini et al. 2008b Self-deployment of mobile sensors on a ring posed the problem, current best, lower bound
  • Maiti et al. 2026c preprint Min-Sum Uniform Coverage Problem by Autonomous Mobile Robots further reading
Partitioning 5 papers
Cited only in the model description 15 papers
  • Peleg 2005 Distributed Coordination Algorithms for Mobile Robot Swarms: New Directions and Challenges
  • Efrima & Peleg 2007 Distributed Models and Algorithms for Mobile Robot Systems
  • Flocchini et al. 2012a Distributed Computing by Oblivious Mobile Robots
  • Flocchini et al. 2019b On Memory, Communication, and Synchronous Schedulers When Moving and Computing
  • Das et al. 2021b preprint Power of Finite Memory and Finite Communication Robots under Asynchronous Scheduler
  • Buchin et al. 2025 On the computational power of energy-constrained mobile robots
  • Das et al. 2024 The Computational Landscape of Autonomous Mobile Robots: The Visibility Perspective
  • Feletti et al. 2025 Computational power of autonomous robots: Transparency vs. opaqueness
  • Nakajima et al. 2024 Efficient Self-stabilizing Simulations of Energy-Restricted Mobile Robots by Asynchronous Luminous Mobile Robots
  • Sharma et al. 2025a Computational Power of Mobile Robots: Discrete Version
  • Cicerone et al. 2025c Brief Announcement: On the Impact of Unlimited Computational Power in OBLOT: Consequences for Synchronous Robots on Graphs
  • Kitamura et al. 2025 preprint Two-Robot Computational Landscape: A Complete Characterization of Model Power in Minimal Mobile Robot Systems
  • Naito et al. 2025 Beyond Pairwise Comparisons: Unveiling Structural Landscape of Mobile Robot Models
  • Feletti et al. 2026b Computational Power of Energy-Constrained Autonomous Robots Under Sequential Schedulers
  • Ninomiya et al. 2026 preprint A Minimum Exact Disjunctive Basis for Two-Robot Solvability Profiles