A Characterization of Semi-Synchrony for Asynchronous Robots with Limited Visibility, and its Application to Luminous Synchronizer Design
Abstract
A mobile robot system consists of anonymous mobile robots, each of which autonomously performs sensing, computation, and movement according to a common algorithm, so that the robots collectively achieve a given task. There are two main models of time and activation of the robots. In the semi-synchronous model (SSYNC), the robots share a common notion of time; at each time unit, a subset of the robots is activated, and each performs all three actions (sensing, computation, and movement) in that time unit. In the asynchronous model (ASYNC), there is no common notion of time, the robots are activated at arbitrary times, and the duration of each action is arbitrary but finite.
In this paper, we investigate the problem of synchronizing ASNYC robots with limited sensing range, i.e., limited visibility. We first present a sufficient condition for an ASYNC execution of a common algorithm to have a corresponding SSYNC execution of ; our condition imposes timing constraints on the activation schedule of the robots and visibility constraints during movement. Then, we prove that this condition is necessary (with probability ) under a randomized ASYNC adversary. Finally, we present a synchronization algorithm for luminous ASYNC robots with limited visibility, each equipped with a light that can take a constant number of colors. Our algorithm enables luminous ASYNC robots to simulate any algorithm , designed for the (non-luminous) SSYNC robots and satisfying visibility constraints.
Keywords. mobile robots, synchronization, limited visibility, light.
1 Introduction
Distributed computing by mobile computing entities has attracted much attention in the past two decades and many distributed system models have been considered, for example, the autonomous mobile robot system [17] modeled after cheap hardware robots with very weak capabilities, the population protocol model [1] motivated by delay tolerant networks, the programmable particle model [4] inspired by movement of amoebae, and the metamorphic robotic system [9, 10] considering modular robots. The computational power of these distributed systems has been investigated in distributed computing theory and many fundamental problems have been proposed that require a degree of agreement among the mobile computing entities. Typical problems are leader election [5, 8], which requires the entities to agree on a single entity, gathering [2, 11, 17], which requires the entities to gather at a point not known apriori, and shape formation [7, 9, 15, 17, 19, 20] (also called the transformability problem), which requires the entities to form a specified shape. These results are considered as theoretical foundations in several related areas like ad-hoc networks, sensor networks, robotics, molecular computing, chemical reaction circuits, and so on.
In this paper, we focus on the autonomous mobile robot system. Let be a set of robots. Each robot is an anonymous (indistinguishable) point moving in the 2D space. The robots are silent (communication-less) and do not have access to a global coordinate system. A robot’s behavior is a repetition of Look-Compute-Move cycles: in the Look phase, it observes the positions of the other robots within its visibility range; in the Compute phase, it computes its next position and a continuous route to the next position with a common deterministic algorithm; in the Move phase, it moves to the computed position along the computed route. The essential properties of mobile robot systems are the visibility range, obliviousness, and the timing and activation models. A robot is equipped with its own local coordinate system , which is a right-handed - coordinate system. The origin of is always the current position of , while the unit distance and the directions and orientations of the and axes are arbitrary. The observation at is a snapshot in containing no additional information other than the positions of the robots. If the visibility range of a robot is unlimited, it can observe all robots, otherwise it can observe the robots within its visibility. In Compute, when the input to the common algorithm is the snapshot taken in the preceding Look, we say the robots are oblivious. When the input includes past observations and computations, we say the robots are non-oblivious. In Move, the movement of a robot is rigid when the robot always reaches the next position, and non-rigid when the robot may stop en route after moving a minimum distance (in ) (if the length of the route to the destination is smaller than , the destination is reached).
Three different types of timing and activation models have been proposed: In the fully-synchronous model (FSYNC), at each discrete time , all robots execute a Look-Compute-Move cycle with each of the Look, Compute, and Move completely synchronized. In the semi-synchronous model (SSYNC), at each discrete time , a non-empty subset of robots are activated and execute a Look-Compute-Move cycle with each of the Look, Compute, and Move completely synchronized. For fairness, we assume that each robot executes infinitely many cycles. In the asynchronous model (ASYNC), the robots do not have a common notion of time and the length of each cycle is arbitrary but finite. We also assume fairness in ASYNC. The main difference between SSYNC (thus, FSYNC) and ASYNC is that in SSYNC, all robots simultaneously take a snapshot in Look, while in ASYNC a robot may observe moving robots although the robot cannot recognize which robots are moving.
The effect of obliviousness, asynchrony, and visibility on the computational power of autonomous mobile robot systems has been extensively investigated [8, 12, 15, 17, 19]. Since the only output by the oblivious robots is their geometric positions, a fundamental problem is the pattern formation problem, that requires the robots to form a target pattern from an initial configuration. Existing literature [15, 17, 19]111 An erratum of [15] is available at [16]. showed that the initial symmetry among the anonymous robots determines the set of formable patterns, irrespective of obliviousness and asynchrony. The only exception is the point formation problem of two robots, also called the rendezvous problem. In fact, Suzuki and Yamashita have shown that the rendezvous problem is solved by oblivious FSYNC robots, but cannot be solved by oblivious SSYNC robots [17]. In other words, the rendezvous problem demonstrates the difference between FSYNC and SSYNC (thus, ASYNC). These results consider the robots with unlimited visibility. Yamauchi and Yamashita have shown that limited visibility substantially shrinks the set of formable patterns by oblivious ASYNC robots because the robots do not know their global symmetry [20].
The robots can overcome the limits by distributed coordination or additional capabilities. Di Luna et al. have shown that a constant number of oblivious ASYNC robots can simulate a single non-oblivious ASYNC robot by encoding the memory contents to the geometric positions of the robots [6]. Das et al. have shown that oblivious ASYNC robots with lights can simulate oblivious SSYNC robots [3]. A luminous robot is equipped with a light whose color is changed in every Look-Compute-Move cycle at the end of Compute and observed by other robots. The authors showed that luminous ASYNC robots with a constant number of colors can simulate an algorithm designed for oblivious SSYNC robots. They presented a synchronizer that makes an activated robot accept or reject the current cycle so that the snapshot of an accepted cycle does not contain any moving robot. In an accepted cycle, the robot changes the color of its light to “moving” and moves to the next position computed by . The synchronizer guarantees fairness by making all robots wait with the “waiting” color after it accepts a cycle until all the other robots accept a cycle. All these techniques are heavily based on the fact that robots have unlimited visibility.
In this paper, we investigate synchronization by oblivious ASYNC robots with limited visibility and we make some fundamental contributions.
We start with a formal definition of simulation by mobile robots. A configuration is the set of positions of the robots in and an execution of algorithm from an initial configuration is an infinite sequence of configurations. In SSYNC, an execution is a sequence of configurations , where is the configuration at time . An ASYNC execution is the sequence of configurations where at least one robot takes a snapshot, with for all . Then, the footprint of a robot is the sequence of the positions of the robot in each configuration. We say that two executions and (possibly in different timing and activation models) are similar when the footprints and local observations at the robots are identical.
We then present a sufficient condition for an ASYNC execution to have a similar SSYNC execution, and we also show that the condition is necessary with probability under a randomized ASYNC adversary. The randomized impossibility result is novel, based on a Borel probability measure space for non-rigid movement and asynchronous observations, and it provides a stronger argument than a worst-case (deterministic) analysis. Our condition not only requires snapshots of static robots but also considers a chain of concurrent observations, that cannot be treated separately in a SSYNC execution. The transitive closure with respect to the concurrent observations forms an equivalence relation and cycles of the ASYNC execution are decomposed into equivalence classes. We then introduce a “happened-before” relation among the equivalence classes based on the local happened-before relation at a single robot or a pair of visible robots. Our condition also requires the happened-before relation to form a directed acyclic graph so that we construct a similar SSYNC execution by applying the equivalence classes one by one in the order of one of their topological sort.
We then present a synchronizer for oblivious ASYNC luminous robots to simulate an execution of an algorithm for oblivious (non- luminous) SSYNC robots satisfying those conditions, as well as some limitations of synchronizers that make use of visible lights.
Related work. Existing literature established a rich class of distributed problems for mobile robot systems. The pattern formation problem [17] is one of the most important static problems, that is, the robots stop moving once they reach a terminal configuration. Suzuki and Yamashita showed the oblivious FSYNC robots can solve the rendezvous problem, while the oblivious SSYNC robots cannot [17]. Flocchini et al. further discussed the rendezvous problem to show the power of lights. A robot with externally visible light can change but cannot see the color of its own light, while the other robots can observe it. A robot with internally visible light can change and see the color of its own light, while the other robots cannot observe it. They showed that the ASYNC robots with externally visible lights can solve the rendezvous problem, while the SSYNC robots with internally visible lights cannot [13]. To demonstrate computational power of the luminous robots, many dynamic problems has been proposed. Das et al. proposed the oscillating points problem, that requires the robots to alternately come closer and go farther from each other [3]. This problem shows the difference between luminous ASYNC robots and (non-luminous) FSYNC robots. Flocchini et al. examined the power of internally visible lights and that of externally visible lights in FSYNC and SSYNC with a variety of static problems such as triangle rotation, center of gravity expansion, and dynamic problems such as the perpetual center of gravity expansion and shrinking rotation [14].
Synchronization was first presented in [3] to overcome the limit of the ASYNC robots with unlimited visibility. In this paper, we further investigate synchronization to demonstrate the difference between ASYNC and SSYNC with limited visibility.
Organization. We provide detailed definitions of ASYNC and SSYNC executions and the similarity between two executions in Section 2. We then provide a sufficient condition for an ASYNC execution to have a similar SSYNC execution, and investigate its necessity under a randomized ASYNC adversary in Section 3. Section 4 provides a synchronizer algorithm for oblivious luminous ASYNC robots that satisfy the necessary and sufficient conditions. We conclude our paper in Section 5.
2 Preliminary
We investigate a system of anonymous oblivious mobile robots in the 2D space. We use just for notation. We assume that at most one robot can occupy any given position at any time.222 We can remove this assumption with multiplicity detection capability.
We consider SSYNC and ASYNC as the semi-synchronous scheduler and the asynchronous scheduler , respectively. We regard a scheduler as a set of schedules that it can produce. Consider an infinite execution of a deterministic algorithm from an initial configuration under . Independently of and ,333 Thus, produces any schedule including the worst-case schedule for and . nondeterministically produces a schedule , which specifies for each when it is activated and executes Look-Compute-Move cycles.
Formally, is a set of schedules for each robot , where is an infinite sequence of Look-Compute-Move cycles. The th cycle of is denoted by a triple , where , , and are the time instants that takes a snapshot in the Look, starts and ends the Move, respectively. We assume that the time interval assigned to is , and for all . Scheduler is fair in the sense that each satisfies that, for any , there is a such that , where and are the sets of positive real numbers and non-negative integers, respectively. The execution is not uniquely determined by , , and , due to the only source of non-determinism, that is, non-rigid movement of robots. The set of possible executions of given , , and is denoted by .
The visibility range of each robot is the unit distance of the global coordinate system .444The common visibility range does not promise common unit distance among the robots. The snapshot taken by at in is the set of positions of robots in visible from at . always contains its origin, because it is the position of in . The number of robots visible from at is denoted by . Let and .
The position of at in the global coordinate system is denoted by . Note that cannot recognize its position . The footprint of in is and let be the set of footprints of all robots of . Since the system is not rigid, is not uniquely determined by , , , , and .
Let and . If the system is rigid, if . Otherwise, for some , , and , there are executions such that and .
Example 1
When , no matter where goes, . When , suppose that and are initially at and , respectively, and synchronously move in parallel along the -axis of at the same speed. As long as their tracks are truncated at the same -coordinate, independently of where they are truncated, for all and .
We say two executions and are similar, denoted by , if and .
Without loss of generality, we assume that produces a schedule such that every cycle has a form for some .
3 ASYNC execution with a similar SSYNC execution
We provide a necessary and sufficient condition for an ASYNC execution to have an SSYNC execution such that and for some .
3.1 Sufficiency
In an SSYNC execution, at each discrete time the activated robots execute a Look-Compute-Move cycle with each of the three phases completely synchronized. From this definition, we directly obtain the following three assumptions on an ASYNC execution to have a corresponding SSYNC execution. (i) No robot observes other robots moving. (ii) When two robots observe each other, they are activated at the same time in a SSYNC execution. (iii) Due to limited visibility, the above “mutually observed” relationship is transitive. We formally describe these assumptions.
Let be the set of robots visible from at time . Then, and . Recall that cannot recognize the correspondence between and . We say that is stationary, if every snapshot in is “stationary” in the sense that does not observe another robot in its move phase. Formally, is stationary if holds for any pair of cycles and such that .555We exclude and , because is not moving at these time instants.
Assumption 1
We assume that is stationary.
Let and be two cycles such that and . If and , we say that and overlap each other.
We say and are concurrent, denoted by , if one of the following conditions holds:
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1.
and .
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2.
, , , and (thus, ).
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, , , and (thus, ).
The concurrency relation is symmetric and reflexive, but is not always transitive. By definition, we have the following proposition.
Proposition 1
For any , , and , if and only if .
If and and are concurrent, then they overlap each other. Moreover, both and observe each other in and , respectively. If and overlap each other, the two robots do not always observe each other. However, at least one of them observes the other.
We say that is pairwise aligned, if two cycles and that overlap each other are always concurrent.
Assumption 2
We assume that is pairwise aligned.
Let be the transitive closure of , that is an equivalence relation on . We abuse the term so that and are concurrent if . Since is not always transitive, may hold. Let be the equivalence class partition of with respect to . Intuitively, the cycles in each must be executed at the same time in the corresponding SSYNC execution. However, because is transitive, we need to consider observations, i.e., and for each .
Let denote the Euclidean distance between two points and in . We say that is consistent, if the following conditions hold for any pair of cycles and such that :
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if and only if .
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If , or equivalently , .
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If , or equivalently , .
Assumption 3
We assume that is consistent.
The following proposition is an extension of Proposition 1.
Proposition 2
Suppose that is stationary, pairwise aligned, and consistent. For any , , and , if and only if .
Proof. If , for any . Otherwise, suppose that holds. Since for any and , by the consistency, which is a contradiction by Proposition 1. Thus, if , then .
Let be two cycles. We say that happens immediately before , denoted by , if one of the following conditions holds:
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and .
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, and .
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, and .
We may call a “happened-before” relation on , because denotes the fact that happens before . Thus, is neither reflexive nor symmetric. It is worth emphasizing that in general, is not transitive either, because it is defined based on the visibility relation between robots like .
Note that if , then , because they do not overlap each other. Note also that either or holds, but generally not both.
Proposition 3
Suppose that is stationary, pairwise aligned, and consistent. No equivalence class of contains cycles and such that .
Proof. Let and be any cycles in , i.e., . We assume that .
If , then by Proposition 2, which is a contradiction. Thus, . Since , either or holds. This implies by the consistency. This is a contradiction.
Let and be two equivalence classes of . If there are cycles and such that , we use the notation . By Proposition 3, binary relation is not reflexive. We say that is serializable if the infinite graph is acyclic.
Assumption 4
We assume that is serializable.
While can be considered as a “happened-before” relation on cycles, it is not adequate to consider as a “happened-before” relation on the equivalence classes. There can be cycles and such that even if . In fact, and may hold at the same time. There is a stationary, pairwise aligned, and consistent execution which is not serializable.
The following proposition is clear by definition.
Proposition 4
Suppose that is stationary, pairwise aligned, consistent, and serializable. If and are in an equivalence class for some , , and , then . That is, each contains at most one cycle for every robot .
Let be an execution of algorithm from initial configuration under a schedule and assume that is stationary, pairwise aligned, consistent, and serializable. Let be the acyclic graph obtained from . Without loss of generality, let be a topological sort of . Let , keeping in mind that is unique for each by Proposition 4.
We construct a schedule from . Intuitively, activates all robots in at time for all . Formally, , where if for . Obviously, is well-defined and .
Consider . Let and be the set of robots visible from at and the snapshot that takes at , respectively. Then, . Let be the position of in at . Then, .
Finally, we need to examine each pair of cycles and assigned to different discrete time in . Thus, . Consider the case where and for and executes no cycle during . Thus, does not move during the time period in . If observes in , and do not overlap each other. Otherwise, contains . If does not observe in , . Otherwise, need to move during the time period in . We describe this situation with the notion of naturality.
For any and , there is a such that , where , , and .666We consider if and is not defined. A topological sort is natural if the following conditions hold for any pair of such cycles and :
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Suppose that . Thus and do not overlap each other. Then .
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Suppose that . Then .
We say that is natural if has a natural topological sort.
Assumption 5
We assume that is natural.
Theorem 1
Proof. We construct an execution as follows: If moves in from to in , we move in from to to construct that satisfies the conditions. We guarantee its feasibility by showing that and . Indeed, if , , and can move from to during in , then it can move to the same position in .
Suppose that a topological sort is natural. It suffices to show the following claim for all : For any robot , , , , and hold. The proof is by induction on .
Base case (). Let be any robot. Then, holds for some and obviously . We show that . Recall that .
Let be any robot. Suppose that , i.e., and . If , then by the consistency, which implies . Otherwise, i.e., , , and thus by the consistency. Thus, .
Next, suppose that . Then, for some . If , then by the naturality. Since and do not move until , . Otherwise, i.e., , by the naturality. Thus, .
Since and start with the same initial configuration , for any , , and hence since . Since is reachable from by algorithm when the snapshot is , is reachable from by algorithm when the snap shot is . Thus, to construct , we move to from in .
Induction step. Assume that the claim holds for all , and we show that the claim folds for . Let be any robot such that .
If , then and by the induction hypothesis. Indeed, for all and , if for some , by the induction hypothesis.
Consider any robot . Since is stationary, is not in move phase at . Suppose for some . Then, and , where for some . Since (i) , (ii) because , and (iii) the position of (in ) at is , we have and , where and are positions of (in ) in and , respectively. Here we use the fact that either or holds, and holds, where .
Suppose otherwise for some . Then and . Let and . Then, . Thus, by the induction hypothesis. Since is the position of at and , , and .
Finally, consider any robot to confirm . If there is a such that , by the consistency. This implies that by the induction hypothesis.
Otherwise, for all . Let be an integer such that , where , , and . By the naturality, , which implies that by the induction hypothesis.
Since , the position of in and (both in ) are the same, i.e., . By the same reason as the base case, to construct , we move to from in .
3.2 Necessity
The conjunction of conditions in Assumptions 1, 2, 3, 4, and 5 is a sufficient condition for an ASYNC execution to have a similar SSYNC execution for some . However, it is not necessary in general. Suppose that does not move any robot. Then, the robot system is at its initial configuration forever, and every execution has a similar SSYNC execution , regardless of whether or not satisfies each of the five assumptions. We will show the necessity of the five assumptions assuming a randomized adversary that determines rigid movement and when moving robots are observed.
Let in be the route of computed by in given snapshot in as input. We assume that is a simple curve such that , where a curve is said to be simple if it does not contain an intersection. Recall that never stops en route if . Otherwise, travels an arbitrary initial part of such that at an arbitrary (possibly variable) speed during Move in . Thus, another robot can observe at any position in (thus, ) at if and .
Intuitively, we assume that satisfying is chosen “uniformly at random” and that is chosen “uniformly at random” from (provided that ). Then, we show that if an execution has a similar execution for some , then satisfies each of the five conditions in Assumptions 1, 2, 3, 4, and 5 with “probability” .
3.2.1 Stationarity
For a fixed algorithm and initial configuration , we show that the stationarity is necessary. To make the argument simple, we assume that the system is rigid. Then, is uniquely determined by if is stationary.
Consider any schedule that contains a unique pair of cycles and such that . Let be the route (in ) that at computes given (in ). Then, is uniquely determined by the position of at (regardless of whether or not is visible from at ). We normalize by , where is the prefix of before (and including) . Then, there is a one-to-one correspondence between and , because is a simple curve. We use the notation to emphasize that is determined by .
We use a Borel measurable space , where is the Borel -algebra on , i.e., the smallest -algebra containing all open intervals in . Then, the probability measure for each element of is the (1-dimensional) Lebesgue probability measure on , that satisfies, for all intervals where , . Thus, and are -null sets. In the following, we consider a probability space .
Let be the set of positions in visible from at , and let be the set of s corresponding to the ’s in . Then, is not stationary if and only if . Since is continuous, . We assume that the probability that is . Thus means that is not visible from at and hence the stationarity is not violated with probability 1 (since we assume that the value of is chosen uniformly at random from ). Here and in the rest of this section, whenever we say that an execution violates a condition, we assume that the condition is violated with positive probability. That is, we assume here that ; the violation occurs with a positive probability.
Lemma 2
Suppose that . Then, for any -almost everywhere on , i.e., does not have a similar SSYNC execution for all , except for a countable number of exceptions.
Proof. Clearly, if provided . Thus, is uncountable since .
On the other hand, is a countable set. Since the system is rigid, is uniquely determined by (because and are fixed), is countable. Thus, only for a countable number of executions , there are similar SSYNC executions .
The above lemma states the following: If the probability that is visible from at is not , then the probability that has a similar SSYNC execution is , under the condition that is indeed visible from at and the stationarity is indeed violated.
We extend Lemma 2 to the case in which contains more than one pair of cycles and such that . We order the pairs of cycles in the increasing order of , where a tie is resolved arbitrarily. We use the concepts and notations above, let and be the position of in at and its normalization, respectively. Let be the set of ’s such that is visible from , i.e., is visible from at . Note that depends on some of in general. Since the system is rigid, is uniquely determined by . We use the notation to emphasize this fact.
Suppose that in when for . By assumption the value of is chosen uniformly at random from . If randomly chooses a value in , by Lemma 2, regardless of the values chosen for , for any -almost everywhere on , i.e., does not have a similar SSYNC execution for all , except for a countable number of exceptions.
Thus, if violates the stationarity, it is unlikely that has a similar SSYNC execution (even under rigid system). In the following, we then assume that is stationary, and investigate the non-rigid system.
3.2.2 Pairwise alignment
To treat the non-rigid system, we use an infinite product measure space . Here is the Cartesian product of a countable infinity of copies of . The family of events is the Cartesian product of a countable infinity of copies of the Borel -field , which is the -algebra generated by all sets in represented by a finite union of cylinders, where a cylinder is a set in with a form for some natural number and for all . Finally is the product measure on , which is defined by , for all .777 We can construct by the Kolmogorov extension theorem in the same manner as Theorem 2.4.4 of the book [18] by Tao.
Let be a property (i.e., predicate) on and let . If is a -null set for , we say that holds -almost everywhere on , which means that holds with probability under on .
We associate a random variable with the th dimension of . If in , there is a finite set such that is uniquely determined by , then .
Let be any schedule and consider . Suppose that in cycle , a robot , which is located at (in ) at time , takes a snapshot (in ), computes a route (in ) by , and moves along in its Move. Since the system is not rigid, it may stop en route after tracing an initial part of such that . Then, the end point of is of when starts. Thus, may affect the rest of execution including and .
The initial part can be denoted by a real number , i.e., represents such that . Here is well-defined, since is a simple curve and . Since the system is non-rigid, any value can occur, as assumed. More carefully, each of ’s takes a value in uniformly at random, and the probability that is for any .
We order in the increasing order of , where a tie is broken arbitrarily, and fix the ordering. By associating the th with the th variable , we identify with an infinite vector . Then, is determined uniquely by since is stationary. We use notation to emphasize that is determined by . It is easy to observe that if and only if .
Suppose that contains a unique triple of cycles , , and such that and overlap each other and and are mutually visible for . Thus, does not satisfy the pairwise alignment condition. Let be the set of ’s such that does not satisfy the pairwise alignment condition. We assume by the same reason we mentioned immediately above Lemma 2.
Lemma 3
Suppose that . Then, does not have a similar execution for any -almost everywhere on .
Proof. Let be the set of vectors such that has a similar SSYNC execution . We show that .
Suppose that has a similar SSYNC execution for some . Let , , and , where . We have the following four cases: (i) , (ii) , (iii) , and (iv) .
When . At , is at and is at in , and at , is at and is at in . Since , , does not have a similar SSYNC execution , since moves at least distance and is simple.
When . By the argument above, does not have a similar SSYNC execution unless . Let be the minimum integer such that . At , is at and is at in , and at , is at and is at in . Since , . That is, is uniquely determined by . (If there is no such , does not have a similar SSYNC execution .) Thus, .
When . Let be the minimum integer such that . By the same argument above, we have . That is, must be uniquely determined by . (If there is no such , does not have a similar SSYNC execution .) Thus, .
When . By the same argument above, we have .
Thus, the proof completes.
In order for to be in , needs to satisfy both and , or equivalently, and . However, unlike the proof for stationarity, does not follow in general, since there may be a pair of an algorithm and an initial configuration such that for any , in , either or holds. If , satisfies the pairwise alignment condition with probability , and we do not consider as an instance that does not satisfy the pairwise alignment condition, even though there is a such that and in .
Note that the same claim as Lemma 3 holds for such that there are more than one triple , , and that violates the pairwise alignment condition. Suppose that contains a pair of cycles and such that . Let be the set of ’s such that satisfies this condition.
Proposition 5
Suppose that . If has a similar execution for some , then -almost everywhere on .
Proof. Let be the set of vectors such that there is a satisfying
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1.
, and
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.
Then, by the same argument in the proof of Lemma 3, we have .
3.2.3 Consistency
Throughout this section, we assume that contains a pair of cycles and such that , and that has a similar execution for some . Let be the set of ’s such that satisfies this condition.
Lemma 4
Suppose that . If which contains a pair of cycles and such that holds has a similar execution for some , then -almost everywhere on .
Proof. Since , there are cycles () such that and , and for all . By Proposition 5, if has a similar SSYNC execution , then, for , -almost everywhere on , which implies that -almost everywhere on .
Lemma 5
Suppose that . If has a similar execution for some , then if and only if , -almost everywhere on .
Proof. Since , has a similar execution for some such that -almost everywhere on by Lemma 4.
Suppose that . Let (resp. ) be the cycle of (resp. ) such that (resp. ) be the position of (resp. ) at (resp. ). Since , , , , , and , provided that . Obviously, the set of satisfying (resp. ) is the -null set when (resp. ). Thus, if and only if -almost everywhere on .
The case is analogous and the case is trivial.
By the proof of above lemma, we have the following corollary.
Corollary 1
Suppose that . If has a similar execution for some , and and hold, then -almost everywhere on .
Lemma 6
Suppose that . If has a similar execution for some , and and hold, then -almost everywhere on .
Proof. Since , has a similar execution for some , such that -almost everywhere on by Lemma 4.
If , since , , and -almost everywhere on , there is a robot and cycle such that is at position at time , since . Such event does not occur -almost everywhere on . Thus, -almost everywhere on .
3.2.4 Serializability
Lemma 7
If which contains a pair of cycles and such that holds has a similar execution for some , then -almost everywhere on .
Proof. If , since , by definition.
Suppose that . Then, and . Then, by a similar argument in the proof of Lemma 3, does not have a similar execution for any such that -almost everywhere on .
Corollary 2
If does not satisfy the serializability, then does not have a similar execution for any -almost everywhere on .
3.2.5 Naturality
Recall that and the set of topological sorts of are determined by execution . We sometimes associate with and to emphasize that they are uniquely determined by . Let be the set of schedules constructed from topological sorts in . By Lemma 4 and Lemma 7, if is similar to , then -almost everywhere on , provided that . Here is the set of such that .
Consider any schedule . Suppose that there is a pair of cycles and such that , where, under , , , and . Obviously, .
First assume that in , and let be the set of such that satisfies this condition.
Lemma 8
Suppose that . If has a similar execution , then -almost everywhere on .
Proof. By definition, is at position at time in . Since , . Suppose that . There is a cycle such that is at at for some . Since , , and , there is a robot (which may be ) and a cycle such that . Thus, does not have a similar execution -almost everywhere on .
Next assume that in , and let be the set of such that satisfies this condition.
Lemma 9
Suppose that . If has a similar execution , then -almost everywhere on .
Proof. Suppose that . Since is at at in , and , . There is a robot for some and a cycle such that is at at in , since . Thus, does not have a similar execution -almost everywhere on .
Consequently, we have the following theorem.
Theorem 10
Each of the five properties, stationarity, pairwise alignment, consistency, serializability, and naturality is necessary for an execution to have a similar execution for some schedule with probability .
4 Luminous synchronizer for ASYNC robots
In this section, we present a synchronizer for oblivious luminous ASYNC robots, that produces ASYNC executions satisfying Assumptions 1, 2, 3, 4, and 5. When the robots are not equipped with lights, each robot cannot recognize which robots are moving. We compensate for this weak capability by a single light at each robot.888 Remember that the color of a light is changed at the end of a Compute, and it is kept until the end of the Compute of the next cycle.
Let be the set of colors that a light can take. Each light initially takes Black (). When robot takes a snapshot at time , is the set of pairs for each visible for at , where is the the position of at in and is the color of ’s light at . Let , and . That is, is the set of positions occupied by robots visible for in , and is the set of colors visible for . Recall that is aware of its color, i.e., the color of its light since and the robots occupy distinct points.
For an initial configuration of the system of non-luminous robots, let be an initial configuration of the system of luminous robots. By definition, and .
We now define a luminous synchronizer on a robot under any schedule . Given any algorithm and initial configuration for non-luminous robots, the initial configuration for is the corresponding configuration . Luminous synchronizer on inhibits on-the-fly ’s motion in some cycle so that the resulting execution have a similar SSYNC execution. Precisely, works as follows: In a cycle of robot , takes a snapshot at time in Look. In Compute, depending on , on first decides whether or not it “accepts” , and then computes a move route . If it accepts , is the one that computes given ; otherwise, if it “rejects” , is the point . Finally, it decides a color and the color of ’s light is changed to at the end of Compute. Thus, the color is visible from other robots at and thereafter. In Move, traces but it may stop en route after moving distance .
The set of executions of for and under scheduler is denoted by . Let be the set of cycles accepted by in . Note that depends on . From (and ), we can construct an execution for non-luminous robots by first extracting the behaviors of the robots for cycles in and then ignoring the colors of lights. Since the next position is computed from (not from ) and the robots do not change their positions in rejected cycles, indeed .
We say that luminous synchronizer is correct if the following conditions hold for any , , , and .
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1.
is fair.
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4.1 Limit of color-based synchronizer
The visibility graph of a configuration of the robots consists of a set of vertices corresponding to the robots and a set of edges between any pair of robots within distance (in ). A visibility preserving algorithm guarantees that the visibility graph does not change in any execution. Formally, an execution for non-luminous robots is visibility preserving, if the following condition holds: For any and , and for any time , if and only if , where is the position of at time in . We say that an algorithm is visibility preserving, if every execution is visibility preserving for any and .
Let be the snapshot taken by a robot at in . Clearly, . Let . In general, a luminous synchronizer on can use the full information on to decide whether it accepts or not. A luminous synchronizer is color-based if it uses and for the selection. A color-based synchronizer is greedy if it accepts if and only if , i.e., and is either or . We show that any greedy synchronizer is not powerful enough in general.
Lemma 11
There exists a rigid system of five luminous robots such that for any greedy synchronizer , there is a triple such that, for some execution , is not consistent.
Proof. Consider a system of five luminous robots. We illustrate an initial part of an execution . Initially, are at , , , , and , respectively in . Thus, . Since the visibility range is , and are visible from , but and are not visible from . The first cycles in are , , , , and . Recall that the initial color of light is . Since is greedy, it accepts , , and .
Suppose that moves to in . Then, at time , is not visible from . Thus, all robots visible from is still have color , and on accepts .
Observe that . Then, is not consistent regardless of the rest of , because but .
We extend Lemma 11 to the color-based synchronizer. A fully synchronous scheduler produces a schedule such that for all and . Thus, in the sense that if , holds.
Theorem 12
There exists a rigid system of five luminous robots such that for any color-based synchronizer , there is a triple such that, for some execution , is not consistent.
Proof. Remember the execution for five luminous robots in the proof of Lemma 11. The counter example relies on the assumptions that is greedy and that the initial color of each robot is . We show that there is a which changes the configuration to a one such that on each robot accepts the current cycle.
Consider an execution , where . Then, all lights have the same color when the robots simultaneously start their th cycles for any since is color-based. Hence, there is a such that each robot accepts since is fair. That is, on each robot accepts , since it accepts a cycle when and . We assume without loss of generality that is the first cycle accepted by .
Now the configuration is immediately before the th cycle starts at time . We replace for each with
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,
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,
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,
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, and
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.
Then, the same argument as the proof of Lemma 11 concludes the theorem.
The above example shows that there exists no color-based synchronizer that works correctly if the algorithm is not visibility preserving.
4.2 Color-based synchronizer for vicinity preserving algorithms
Lemma 11 and Theorem 12 demonstrate that there is no color-based synchronizer for an arbitrary visibility preserving algorithm. Moreover, it is difficult for oblivious luminous robots to satisfy the second condition of naturality, because it requires remembering the positions of other robots. In this section, we consider algorithms with more restricted changes in the visibility graph and propose a color-based synchronizer for such algorithms.
An execution for non-luminous robots is vicinity preserving, if the following condition holds: For any , , and for any time , if and only if . In other words, has to stay in the vicinity of its initial position. We say that an algorithm is vicinity preserving, if every execution is vicinity preserving for any and . In this section, we propose a color-based synchronizer that uses a set of colors and show its correctness provided that is vicinity preserving and designed for non-luminous SSYNC robots. We describe as a finite-state machine with a state set , an input alphabet , and an output alphabet . When is executed on , the sate of is the color (of the light) of and the input is the set of colors of the robots visible from , excluding ’s color. Table 1 shows the transition function and the output function of , where
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means any such that ,
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means any such that , and
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means any such that and .
The initial state of each robots is . We assume that without loss of generality, the visibility graph of the initial configuration is connected. Since is vicinity preserving, let be the set of neighbors of in the visibility graph which is defined by .
Current state | Input | Next state | Output |
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Bk | accept | ||
reject | |||
R | reject | ||
B | reject | ||
G | reject | ||
W | reject |
Robot is waiting when its state is (Black) and moving when its state is (Red). It rejects the current cycle when its state is and observes another robot in state , and changes its state to (White). It changes its state from to when it does not observe any robot in state . Robot has finished moving when its state is (Blue) and it changes its state to (Green) when the states of robots in are and . Finally, it changes its state to when the states of robots in are and .
Let be any execution of for a vicinity preserving algorithm , and let be the set of accepted cycles (which depends on ). Then, is an execution of non-luminous robots. We will show that has a corresponding SSYNC execution for non-luminous robots. We demonstrate that (i) is fair, (ii) satisfies stationarity, pairwise alignment, consistency, serializability, and naturality.
Lemma 13
is fair.
Proof. We show that every robot reaches state infinitely many times in . We first regard three states , , and as a virtual state . Every robot starts with state . When the state of a robot is (i.e., ) and the sate of each robot is either (i.e., or ) or , then it can change its state to . When the state of is and the state of each robot is either or (i.e., ), then it can change its state to (i.e., ). For the time being, we assume that if the state of is , then it will eventually change its state to . We show that every robot reaches state infinitely many times in . This implies that it reaches state infinitely many times. Then, we can conclude that is fair, because changes its color to in if and only if is accepted by .
Consider any robot . Let , where is the cycle of in which it changes its state for the th time. The state of changes from state to in , from state to in , from state to in , ans so on. For each , denotes the the state that takes for the th time. Thus, the state of changes from state to in , from state to in , from state to in , and so on. Independently of , in , changes its state from to , where if , if , and if .
Let . Then, takes a snapshot at and changes the color of its light (i.e., its state) by . The new color becomes visible from other robots at . Thus, the state of at is and is at .
For any robot , let be the sate of at time . Thus, and . We first claim that for any , is either or for any robot . The proof is by induction on .
When , . Suppose that for some . Then changes its state from to in and . It is a contradiction, because and . Thus, is either or .
Provided that is either or , we show that is either or . By definition, if , then . If , then cannot change its state from to in . Thus, . Suppose that . In time interval , the state of is still . During this interval, the state of is either or . Thus, by the same argument as the base case for , a contradiction is derived.
To show that is an infinite sequence, we assume that it is a finite sequence and derive a contradiction. Let be the length of , i.e., is the last cycle of . By the claim above, is finite for any . Without loss of generality, we assume that is the shortest one. By the claim, the length of is either or , if . Let be a time instant that and all have finished their last cycles in . Since is fair, there is a cycle such that . The state of is at , and the state of each is either or at . Thus, the state of changes in , and hence . It is a contradiction.
We next show that, for all , if the state of a robot is , then it will eventually change its state to in for any . The proof is by induction on .
Base Case (when ): Let . The state of is either or (and not ) as long as the state of is (i.e., either , , or ). Recall the notation for all and .
By , if , then it changes its state either or in . It changes its state to if there is an such that , otherwise it changes its state to .
Suppose that . If for each , since the state of is then changes its state to in . Otherwise, if there is an such that , by the observation above, changes its state to or in , where satisfies . Furthermore, if changes its state state to , it maintains the state as long as the state of is . Thus, eventually changes its state to .
We show that whose state is will eventually change its state to after repeating the loop between and a finite number of times. Suppose that , and let be the set of such that . Robot changes its state to in if and only if . Since , will eventually change its state to after repeating the loop at most times, since any robot with state will never return to as long as the state of is .
Finally, we show that whose state is will eventually change its state to . Suppose that . If for each , then changes its state to in . Otherwise, if there is an such that , it does not change its state in . If , then will eventually change its state to .
If , then changes its state to in , where satisfies . Thus, there is a and such that such that changes its state from to in . Since the total number of times that robots other than repeat the loops is bounded by , will eventually change its state from to . The proof of the base case completes.
Induction Step: Suppose that and for all . Then, changes its state from to in . The state of is as long as there is an such that . We show that will eventually change its state from to . If cannot change its state from , then there is an whose state is neither nor . If the state of is , then it will eventually change its state to . If it is or , we can derive a contradiction, because can change its state to or when the state of is . Thus, we can apply the proof for the base case to complete the induction step.
Lemma 14
is stationary.
Proof. A robot can move in if the cycle is accepted. If is accepted, the state of is during the interval . If a cycle of a robot satisfies that , then is rejected. Thus, is accepted, only if .
Lemma 15
is pairwise aligned.
Proof. Suppose that is not pairwise aligned. In , there are cycles , , and satisfying the following conditions:
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(thus, ),
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and overlap each other,
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and overlap each other, and
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are not accepted (hence, they are not the elements of ).
Since is stationary, we have
Then, for some , there is a cycle where changes its state from to . It is a contradiction since .
Recall that , where is the cycle of in which it changes its state for the th time. Scheduler accepts every cycle in which changes its state from to and accepts no other cycles.
Proposition 6
Suppose that is vicinity preserving. Then if any pair of cycles and in such that (in ) and satisfies that , then is consistent.
Proof. Since is vicinity preserving, if and only if and if .
Lemma 16
is consistent.
Proof. Assume that there are two cycles and in such that (in ) , , and , to derive a contradiction. Let .
Since , there are cycles such that for all , where and . Note that in cycle , changes its state from to . Recall that in the proof of Lemma 13, we showed that is either or . Then in cycles and , and change their states to he same state for the th time for some , since . Thus, in and , and change their states to for the th time.
Since and , we assume that by the stationarity. Since , , which means that there is a cycle for some , in which changes its state from to . Obviously, .
Consider . By definition, . Observe that means that and means that . Thus, , because changes its state to in . Then, there is a cycle such that and , and changes its state from to in .
Consider . By the same argument above, . Thus, or all . It is a contradiction, since changes its state to (in ) from after .
Lemma 17
is serializable.
Proof. Let , where is the equivalence classes of with respect to .
If two cycles and are in the same class , i.e., , as we showed in the proof of Lemma 15, and change their states to the same state for some .
Suppose that . Then, in , changes its state to for some , and in , changes its state to for some . By definition, , which implies .
If is not serializable, there is a loop in , which is a contradiction.
Lemma 18
Suppose that is vicinity preserving. is natural.
Proof. Let be any topological sort of . Consider any pair of cycles and in such that (in ) , where , , and . (We assume that when for the consistency.)
If , then since is vicinity preserving.
Suppose that . If , since , , which is a contradiction since . Thus, .
Theorem 19
For any vicinity preserving algorithm for non-luminous SSYNC mobile robots, color-based synchronizer is correct.
5 Conclusion
In this paper, we investigated synchronization by ASYNC robots with limited visibility. We started with a sufficient condition for an ASYNC execution to have a similar SSYNC execution. Our condition consists of stationarity, pairwise alignment, consistency, serializability, and naturality on the timing of Look-Compute-Move cycles and visibility relation among the robots. We then showed the necessity of the five properties under a randomized adversary that selects non-rigid movement and asynchronous observations of the robots. Our randomized impossibility argument is a novel and stronger technique than a worst-case (deterministic) analysis. Finally, we presented a color-based synchronizer for luminous ASYNC robots together with the limit of color-based synchronizers. We showed that there exists an algorithm for which no color-based synchronizer can guarantee the five properties, if the algorithm is not visibility preserving. Then, we provided a color-based synchronizer that, for a given vicinity preserving algorithm , produces an ASYNC execution that satisfies the five properties. Thus, luminous ASYNC robots can simulate vicinity preserving algorithms designed for (non-luminous) SSYNC robots. There are important open problems about a necessary and sufficient condition for an algorithm to have a luminous synchronizer. The requirement of our color-based synchronizer is that an algorithm is vicinity preserving. It is open whether there exists a color-based synchronizer and a general luminous synchronizer that works for visibility preserving algorithms.
Acknowledgment
The authors would like to thank Prof. Toshio Nakata for his precious comments on the infinite product of a Borel probability measure space and Prof. Giovanni Viglietta for precious discussion in University of Ottawa.
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