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On the design of generalist strategies for swarms of simulated robots engaged in a task-allocation scenario

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Abstract

This study focuses on issues related to the evolutionary design of task-allocation mechanisms for swarm robotics systems with agents potentially capable of performing different tasks. Task allocation in swarm robotics refers to a process that results in the distribution of robots to different concurrent tasks without any central or hierarchical control. In this paper, we investigate a scenario with two concurrent tasks (i.e. foraging and nest patrolling) and two environments in which the task priorities vary. We are interested in generating successful groups made of behaviourally plastic agents (i.e. agents that are capable of carrying out different tasks in different environmental conditions), which could adapt their task preferences to those of their group mates as well as to the environmental conditions. We compare the results of three different evolutionary design approaches, which differ in terms of the agents’ genetic relatedness (i.e. groups of clones and groups of unrelated individuals), and/or the selection criteria used to create new populations (i.e. single and multi-objective evolutionary optimisation algorithms). We show results indicating that the evolutionary approach based on the use of genetically unrelated individuals in combination with a multi-objective evolutionary optimisation algorithm has a better success rate then an evolutionary approach based on the use of genetically related agents. Moreover, the multi-objective approach, when compared to a single-objective approach and genetically unrelated individual, significantly limits the tendency towards task specialisation by favouring the emergence of generalist agents without introducing extra computational costs. The significance of this result is discussed in view of the relationship between individual behavioural skills and swarm effectiveness.

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Notes

  1. A more comprehensive discussion of the advantages of the aclonal over the clonal approach can be found in Tuci and Trianni (2014).

  2. Note that this is just a linguistic description of the task-allocation process required by this scenario. This description should not be interpreted as an operational illustration of the agents’ behaviour.

  3. In all post-evaluation tests described in this Section, each single group undergoes a set of \(E=80\) differently seeded t-sequences (40 ABA-sequence, and 40 BAB-sequence), each made of \(V=3\) trials, for a total of 240 trials, 120 trials in Env. A and 120 trials in Env. B. Each t-sequence differs from the others in the initialisation of the random number generator, which influences the agents initial position and orientation at trial 1 and during repositioning, all the randomly defined features of the environment, and the noise added to motors and sensors.

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Authors and Affiliations

  1. Llandinam Building, Computer Science Department, Aberystwyth University, Aberystwyth, SY23 3DB, UK

    Elio Tuci & Alexandre Rabérin

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  1. Elio Tuci
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  2. Alexandre Rabérin
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Correspondence to Elio Tuci.

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Tuci, E., Rabérin, A. On the design of generalist strategies for swarms of simulated robots engaged in a task-allocation scenario. Swarm Intell 9, 267–290 (2015). https://doi.org/10.1007/s11721-015-0113-y

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