A New Approach for Analyzing Convergence Algorithms for Mobile Robots

Cord-Landwehr, Andreas; Degener, Bastian; Fischer, Matthias; Hüllmann, Martina; Kempkes, Barbara; Klaas, Alexander; Kling, Peter; Kurras, Sven; Märtens, Marcus; Heide, Friedhelm Meyer auf der; Raupach, Christoph; Swierkot, Kamil; Warner, Daniel J.; Weddemann, Christoph; Wonisch, Daniel

doi:10.1007/978-3-642-22012-8_52

Cited by 48 publications

(26 citation statements)

References 13 publications

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“…On the other hand, [37] introduced anonymous, oblivious, asynchronous, mobile agents which cannot directly communicate, but can occasionally observe the environment. Gathering and convergence [5,[19][20][21], as well as pattern formation [23,25,37,38] were studied for such agents.…”

Section: Related Workmentioning

confidence: 99%

Collecting Information by Power-Aware Mobile Agents

Anaya

Chalopin

Czyzowicz

et al. 2012

Lecture Notes in Computer Science

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Abstract. A set of identical, mobile agents is deployed in a weighted network. Each agent possesses a battery -a power source allowing the agent to move along network edges. Agents use their batteries proportionally to the distance traveled. At the beginning, each agent has its initial information. Agents exchange the actually possessed information when they meet. The agents collaborate in order to perform an efficient convergecast, where the initial information of all agents must be eventually transmitted to some agent. The objective of this paper is to investigate what is the minimal value of power, initially available to all agents, so that convergecast may be achieved. We study the question in the centralized and the distributed settings. In the distributed setting every agent has to perform an algorithm being unaware of the network. We give a linear-time centralized algorithm solving the problem for line networks. We give a 2-competitive distributed algorithm achieving convergecast for tree networks. The competitive ratio of 2 is proved to be the best possible for this problem, even if we only consider line networks. We show that already for the case of tree networks the centralized problem is strongly NP-complete. We give a 2-approximation centralized algorithm for general graphs.

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Section: Related Workmentioning

confidence: 99%

Collecting Information by Power-Aware Mobile Agents

Anaya

Chalopin

Czyzowicz

et al. 2012

Lecture Notes in Computer Science

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“…After each cycle, a robot may be inactive for some time. For more details on the model, see the distributed computing literature on the subject (e.g., [1,3,11,15,24,26,27,46,49,51,52,56]), as well as section 2 in this paper.…”

Section: Settingmentioning

confidence: 99%

“…In the synchronous models (Fsync and Ssync), the cycles of all robots are fully synchronized: the sensors that become active do so all at the same time and each operation of the life cycle is performed by all robots simultaneously [1,6,11,13,19,20,21,29,41,52]. In the asynchronous model (Async), there is no global clock, and the robots do not have a common notion of time; furthermore, the duration of each activity (or inactivity) is finite but unpredictable [15,26,27,36,46].…”

Section: Settingmentioning

confidence: 99%

Distributed Computing by Mobile Robots: Gathering

Cieliebak¹,

Flocchini²,

Prencipe³

et al. 2012

SIAM J. Comput.

204

147

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Abstract. Consider a set of n > 2 identical mobile computational entities in the plane, called robots, operating in Look-Compute-Move cycles, without any means of direct communication. The Gathering Problem is the primitive task of all entities gathering in finite time at a point not fixed in advance, without any external control. The problem has been extensively studied in the literature under a variety of strong assumptions (e.g., synchronicity of the cycles, instantaneous movements, complete memory of the past, common coordinate system, etc.). In this paper we consider the setting without those assumptions, that is, when the entities are oblivious (i.e., they do not remember results and observations from previous cycles), disoriented (i.e., have no common coordinate system), and fully asynchronous (i.e., no assumptions exist on timing of cycles and activities within a cycle). The existing algorithmic contributions for such robots are limited to solutions for n ≤ 4 or for restricted sets of initial configurations of the robots; the question of whether such weak robots could deterministically gather has remained open. In this paper, we prove that indeed the Gathering Problem is solvable, for any n > 2 and any initial configuration, even under such restrictive conditions.

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“…One quite simple and effective convergence solution in Async exploits the Center of Gravity of the robots [13]. With the strongest assumption of unlimited mobility (all robots always reach their destinations when performing a Move), convergence time in Async can be improved [17].…”

Section: Static Problemsmentioning

confidence: 99%

Autonomous Mobile Robots: A Distributed Computing Perspective

Prencipe

2013

Algorithms for Sensor Systems

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Abstract. The distributed coordination and control of a team of autonomous mobile robots is a problem widely studied in a variety of fields, such as engineering, artificial intelligence, artificial life, robotics. Generally, in these areas, the problem is studied mostly from an empirical point of view. Recently, the study of what can be computed by such team of robots has become increasingly popular in theoretical computer science and especially in distributed computing, where it is now an integral part of the investigations on computability by mobile entities [28]. In this paper we describe the current investigations on the algorithmic limitations of what autonomous mobile robots can do with respect to different coordination problems, and overview the main research topics that are gaining attention in this area.

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A New Approach for Analyzing Convergence Algorithms for Mobile Robots

Cited by 48 publications

References 13 publications

Collecting Information by Power-Aware Mobile Agents

Collecting Information by Power-Aware Mobile Agents

Distributed Computing by Mobile Robots: Gathering

Autonomous Mobile Robots: A Distributed Computing Perspective

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