oaioai:cris.unibo.it:11585/110387

Task partitioning in swarms of robots: An adaptive method for strategy selection

Abstract

Task partitioning is the decomposition of a task into two or more sub-tasks that can be tackled separately. Task partitioning can be observed in many species of social insects, as it is often an advantageous way of organizing the work of a group of individuals. Potential advantages of task partitioning are, among others: reduction of interference between workers, exploitation of individuals\u2019 skills and specializations, energy efficiency, and higher parallelism. Swarms of robots can benefit from task partitioning in the same way social insects do. Despite this, few works in swarm robotics are dedicated to this subject. In this paper, we study the case in which a swarm of robots has to tackle a task that can be partitioned into a sequence of two sub-tasks. The sub-tasks interface with each other in an asynchronous way through caches. We propose a method that allows a swarm of robots to decide which strategy to use for tackling the task: a strategy that makes use of task partitioning or one that does not. The method is self-organized, relies on the experience of each individual, and it does not require explicit communication. We evaluate the method in simulation experiments, using a foraging task as testbed. We study cases in which task partitioning is preferable and cases in which it is not. We show that the proposed method leads to good performance of the swarm in both cases, by employing task partitioning only when it is advantageous. We also show that the swarm is able to react to changes in the environmental conditions by adapting on-line the partitioning strategy. Scalability experiments show that the strategy employed by the swarm using the proposed method also varies in relation to the swarm size. This results in good performance across all the tested group sizes

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Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna

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oaioai:cris.unibo.it:11585/110387Last time updated on 9/3/2019

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