Gathering of robots on meeting-points: feasibility and optimal resolution algorithms

Cicerone, Serafino; Stefano, Gabriele Di; Navarra, Alfredo

doi:10.1007/s00446-017-0293-3

Cited by 41 publications

(17 citation statements)

References 74 publications

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“…The choice of the synchronization model very often determines whether a task is feasible or not. Tasks well investigated to this respect are the Gathering, see [5]- [7], [9], [18], [21], [22], [31], [32], [37], [43], [44], [46], in which all robots are required to reach a common destination not known in advance; the Pattern formation, see [4], [11], [33], [40], [41], in which robots are required to form a specific geometric pattern in the Euclidean plane or to suitable dispose on the nodes of a graph; the Leader Election problem, see [8], [19], [20], [33], where one robot (when possible) must be selected and recognized by all the others as the leader; the Exploration problem, see [3], [17], [25], [27], [29], [34]- [36], [47], [48], where the robots are required to visit / explore an area of interest or a graph.…”

Section: Related Workmentioning

confidence: 99%

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“Semi-Asynchronous”: A New Scheduler in Distributed Computing

2021

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The study of mobile entities that based on local information have to accomplish global tasks is of main interest for the scientific community. Classic models for the activation and synchronization of mobile entities are the fully-synchronous (FSYNC), semi-synchronous (SSYNC), and asynchronous (ASYNC) models, where entities alternate between active and inactive states with different timing. According to the assumed synchronization model, very different results have been achieved in the field of distributed computing. One of the main outcomes is the big gap between the ASYNC and the other models in terms of manageability and algorithm design. In fact, there are still many problems for which it is not known whether synchronicity is crucial for designing resolution algorithms or not. In order to better understand the ASYNC case, here we propose a further model referred to as the semiasynchronous (SASYNC). This slightly deviates from SSYNC. In fact, like in SSYNC (and FSYNC), the duration of the activation of an entity is kept of fixed time whereas, like in ASYNC, the starting instant of the activation is not fully synchronized with the possible activation of other entities. We show that for entities moving on graphs, the SSYNC model allows accomplishing more tasks than the SASYNC that in turn allows accomplishing more tasks than the ASYNC. Furthermore, our results show that, especially to tackle problems in the Euclidean plane, the SASYNC model is already quite challenging, therefore there is no need to get involved with complications arising in the ASYNC model.

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Section: Related Workmentioning

confidence: 99%

“…[36]. Along with feasibility, sometimes also optimization issues have been explored, see [5], [7], [9], [31], [32]. Objective functions to accomplish a specific task may refer to the number of robots as in [27], or the number of LCM cycles as in [15].…”

Section: Related Workmentioning

confidence: 99%

“Semi-Asynchronous”: A New Scheduler in Distributed Computing

2021

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“…1 These investigations have addressed questions of a general nature such as which configurations (patterns) are formable from a given initial configuration, from which initial configurations every pattern is formable, and which patterns are formable from any initial configuration [9,10,20,30]. They also have focused on specific classes of important patterns, in particular uniform circle and point [8,13,19]. The importance of point formation problem stems from the fact that it corresponds to the basic coordination task in swarm robotics called Gathering (or Rendezvous), where all robots are required to meet at the same point (not fixed in advance).…”

Section: Related Workmentioning

confidence: 99%

Separating Bounded and Unbounded Asynchrony for Autonomous Robots

Kirkpatrick

Kostitsyna

Navarra

et al. 2021

Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing

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We consider distributed computations, by identical autonomous mobile entities, that solve the Point Convergence problem: given an arbitrary initial configuration of entities, disposed in the Euclidean plane, move in such a way that, for all ε > 0, a configuration is eventually reached and maintained in which the separation between all entities is at most ε. The problem has been previously studied in a variety of settings. Our study concerns the minimal assumptions under which entities, moving asynchronously with limited and unknown visibility range and subject to limited imprecision in measurements, can be guaranteed to converge in this way.We present an algorithm that solves Point Convergence, provided the degree of asynchrony is bounded by some arbitrarily large but fixed constant. This provides a strong positive answer to a decade old open question posed by Katreniak.We also prove that, in an otherwise comparable setting, Point Convergence is impossible with unbounded asynchrony. This serves to distinguish the power of bounded and unbounded asynchrony in the control of autonomous mobile entities, settling at the same time a long-standing question whether in the Euclidean plane synchronous entities are more powerful than asynchronous ones.

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“…In particular, the gathering of two agents, often called rendezvous, has attracted a lot of attention, well documented in [1]. The problem of gathering with the objective of minimizing movements has been studied in [11]. However to the best of our knowledge, there have no previous studies on gathering with fixed constraints (budgets) on energy required for movements.…”

Section: Definition 1 (Near-gathering)mentioning

confidence: 99%

Near-gathering of energy-constrained mobile agents

Bärtschi

Bampas

Chalopin

et al. 2021

Theoretical Computer Science

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We study the task of gathering k energy-constrained mobile agents in an undirected edgeweighted graph. Each agent is initially placed on an arbitrary node and has a limited amount of energy, which constrains the distance it can move. Since this may render gathering at a single point impossible, we study three variants of near-gathering: The goal is to move the agents into a configuration that minimizes either (i) the radius of a ball containing all agents, (ii) the maximum distance between any two agents, or (iii) the average distance between the agents. We prove that (i) is polynomial-time solvable, (ii) has a polynomial-time 2-approximation with a matching NP-hardness lower bound, while (iii) admits a polynomial-time 2(1 − 1 k)-approximation, but no FPTAS, unless P = NP. We extend some of our results to additive approximation.

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Gathering of robots on meeting-points: feasibility and optimal resolution algorithms

Cited by 41 publications

References 74 publications

“Semi-Asynchronous”: A New Scheduler in Distributed Computing

“Semi-Asynchronous”: A New Scheduler in Distributed Computing

Separating Bounded and Unbounded Asynchrony for Autonomous Robots

Near-gathering of energy-constrained mobile agents

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