Swarm Robot Exploration Strategy for Path Formation Tasks Inspired by Physarum polycephalum

Yandong Luo, Jianwen Guo, Zhenpeng Lao, Shaohui Zhang, Xiaohui Yan and Ning Cai

Complexity, 2021, vol. 2021, 1-17

Abstract: Physarum polycephalum, a unicellular and multiheaded slime mould, can form highly efficient networks connecting separated food sources during the process of foraging. These adaptive networks exhibit a unique characteristic in that they are optimized without the control of a central consciousness. Inspired by this phenomenon, we present an efficient exploration and navigation strategy for a swarm of robots, which exploits cooperation and self-organisation to overcome the limited abilities of the individual robots. The task faced by the robots consists in the exploration of an unknown environment in order to find a path between two distant target areas. For the proposed algorithm (EAIPP), we experimentally present robustness tests and obstacle tests conducted to analyse the performance of our algorithm and compare the proposed algorithm with other swarm robot foraging algorithms that also focus on the path formation task. This work has certain significance for the research of swarm robots and Physarum polycephalum. For the research of swarm robotics, our algorithm not only can lead multirobot as a whole to overcome the limitations of very simple individual agents but also can offer better performance in terms of search efficiency and success rate. For the research of Physarum polycephalum, this work is the first one combining swarm robots and Physarum polycephalum. It also reveals the potential of the Physarum polycephalum foraging principle in multirobot systems.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:6698421

DOI: 10.1155/2021/6698421

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