Autonomous Mobile Robots: A Distributed Computing Perspective

Prencipe, Giuseppe

doi:10.1007/978-3-642-45346-5_2

Cited by 16 publications

(7 citation statements)

References 54 publications

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“…Pagli et al [28] study the problem of collision-free GATHERING classical robots to a small area; however, they do not provide a runtime analysis. Similarly, much work on the classical robot model [24,[29][30][31] for GATHERING does not have runtime analysis, except in a few cases [32][33][34][35][36]. Furthermore, Izumi et al [37] considered the robot scattering problem (opposite of GATHERING) in the semi-synchronous setting and provided a solution with an expected runtime of O(min{N, D 2 + log N}); here, D is the diameter of the initial configuration.…”

Section: Related Workmentioning

confidence: 99%

Constant-Time Complete Visibility for Robots with Lights: The Asynchronous Case

2021

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We consider the distributed setting of N autonomous mobile robots that operate in Look-Compute-Move (LCM) cycles and use colored lights (the robots with lights model). We assume obstructed visibility where a robot cannot see another robot if a third robot is positioned between them on the straight line segment connecting them. In this paper, we consider the problem of positioning N autonomous robots on a plane so that every robot is visible to all others (this is called the Complete Visibility problem). This problem is fundamental, as it provides a basis to solve many other problems under obstructed visibility. In this paper, we provide the first, asymptotically optimal, O(1) time, O(1) color algorithm for Complete Visibility in the asynchronous setting. This significantly improves on an O(N)-time translation of the existing O(1) time, O(1) color semi-synchronous algorithm to the asynchronous setting. The proposed algorithm is collision-free, i.e., robots do not share positions, and their paths do not cross. We also introduce a new technique for moving robots in an asynchronous setting that may be of independent interest, called Beacon-Directed Curve Positioning.

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Section: Related Workmentioning

confidence: 99%

Constant-Time Complete Visibility for Robots with Lights: The Asynchronous Case

2021

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“…When it comes to parallel search, most of the literature deals with mobile agents that search graphs of different topologies, and it is typically assumed that agents employ some form of communication. The literature on this subject is vast, and some good references can be found in, e.g., [2,3,10,18,23]. The major difference between our setting and the mobile agent setting, is that we allow "random access" to the different boxes.…”

Section: Related Workmentioning

confidence: 99%

Parallel Bayesian Search with No Coordination

Fraigniaud

Korman

Rodeh

2019

J. ACM

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Coordinating the actions of agents (e.g., volunteers analyzing radio signals in SETI@home) yields efficient search algorithms. However, such an efficiency is often at the cost of implementing complex coordination mechanisms which may be expensive in terms of communication and/or computation overheads. Instead, non-coordinating algorithms, in which each agent operates independently from the others, are typically very simple, and easy to implement. They are also inherently robust to slight misbehaviors, or even crashes of agents. In this article, we investigate the “price of non-coordinating,” in terms of search performance, and we show that this price is actually quite small. Specifically, we consider a parallel version of a classical Bayesian search problem, where set of k ≥1 searchers are looking for a treasure placed in one of the boxes indexed by positive integers, according to some distribution p . Each searcher can open a random box at each step, and the objective is to find the treasure in a minimum number of steps. We show that there is a very simple non-coordinating algorithm which has expected running time at most 4(1−1/ k +1) 2 OPT+10, where OPT is the expected running time of the best fully coordinated algorithm. Our algorithm does not even use the precise description of the distribution p , but only the relative likelihood of the boxes. We prove that, under this restriction, our algorithm has the best possible competitive ratio with respect to OPT. For the case where a complete description of the distribution p is given to the search algorithm, we describe an optimal non-coordinating algorithm for Bayesian search. This latter algorithm can be twice as fast as our former algorithm in practical scenarios such as uniform distributions. All these results provide a complete characterization of non-coordinating Bayesian search. The take-away message is that, for their simplicity and robustness, non-coordinating algorithms are viable alternatives to complex coordinating mechanisms subject to significant overheads. Most of these results apply as well to linear search, in which the indices of the boxes reflect their relative importance, and where important boxes must be visited first.

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“…The literature on this subject is vast, and some good references can be found, e.g., in [2,3,10,16,21]. The major difference between our setting and the mobile agent setting, is that we allow "random access" to the different boxes.…”

Section: Related Workmentioning

confidence: 99%

Parallel Search with No Coordination

Korman

Rodeh

2017

Structural Information and Communication Complexity

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We consider a parallel version of a classical Bayesian search problem. k agents are looking for a treasure that is placed in one of the boxes indexed by N + according to a known distribution p. The aim is to minimize the expected time until the first agent finds it. Searchers run in parallel where at each time step each searcher can "peek" into a box. A basic family of algorithms which are inherently robust is non-coordinating algorithms. Such algorithms act independently at each searcher, differing only by their probabilistic choices. We are interested in the price incurred by employing such algorithms when compared with the case of full coordination.We first show that there exists a non-coordination algorithm, that knowing only the relative likelihood of boxes according to p, has expected running time of at most 10+4(1+ 1 k )2 T , where T is the expected running time of the best fully coordinated algorithm. This result is obtained by applying a refined version of the main algorithm suggested by Fraigniaud, Korman and Rodeh in STOC'16, which was designed for the context of linear parallel search.We then describe an optimal non-coordinating algorithm for the case where the distribution p is known. The running time of this algorithm is difficult to analyse in general, but we calculate it for several examples. In the case where p is uniform over a finite set of boxes, then the algorithm just checks boxes uniformly at random among all non-checked boxes and is essentially 2 times worse than the coordinating algorithm. We also show simple algorithms for Pareto distributions over M boxes. That is, in the case where p(x) ∼ 1/x b for 0 < b < 1, we suggest the following algorithm: at step t choose uniformly from the boxes unchecked in {1, . . . , min(M, ⌊t/σ⌋)}, where σ = b/(b + k − 1). It turns out this algorithm is asymptotically optimal, and runs about 2 + b times worse than the case of full coordination. *

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Autonomous Mobile Robots: A Distributed Computing Perspective

Cited by 16 publications

References 54 publications

Constant-Time Complete Visibility for Robots with Lights: The Asynchronous Case

Constant-Time Complete Visibility for Robots with Lights: The Asynchronous Case

Parallel Bayesian Search with No Coordination

Parallel Search with No Coordination

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