Swarm Robotics: A Survey from a Multi-Tasking Perspective

Author:

Debie Essam1ORCID,Kasmarik Kathryn1ORCID,Garratt Matt1ORCID

Affiliation:

1. University of New South Wales, Canberra, Australia

Abstract

The behaviour of social insects such as bees and ants has influenced the development of swarm robots. To enable robots to cooperate together, swarm robotics employs principles such as communication, coordination, and collaboration. Collaboration among multiple robots can lead to a faster task completion time compared to the utilisation of a single, complex robot. One of the key aspects of swarm robotics is that control is distributed uniformly across the robots in the swarm, which boosts the system’s resilience and fault tolerance. Through the use of the robots’ embodied sensors and actuators, this distributed control often facilitates the emergence of collective behaviours through the interaction of the robots with one another and with the environment. The purpose of this survey is to examine the reasons behind the lack of utilisation of swarm robots in multi-tasking applications, which will be accomplished by studying previous research works in the field. We examine the literature from the perspective of multi-tasking: we pay particular attention to concepts that contribute to the progress of swarm robotics for multi-tasking applications. To do this, we first examine the different studies in multi-tasking swarm robotics, covering platforms, multi-tasking scenarios, sub-task allocation methodologies, and performance metrics. We then highlight several swarm robotics related disciplines that have significant effect on the development of swarm robotics for multi-tasking problems. We propose two taxonomies: the first categorises works based on the characteristics of the scenarios being handled, whereas the second taxonomy categorises works based on the swarming strategies utilised to achieve multi-tasking capabilities. We finish with a discussion of swarm robots’ existing limitations for real-world multi-tasking applications, as well as recommendations for future research directions.

Funder

Australian Research Council Discovery Project

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Reference163 articles.

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5. A Markov chain approach to probabilistic swarm guidance

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