A Physarum-Inspired Decision-Making Strategy for Multisource Task Searching of Mobile Robots

Laihao Jiang, Hongwei Mo and Lifang Xu

Complexity, 2020, vol. 2020, 1-14

Abstract:

In the real world, there are many different kinds of sources, such as light, sound, and gas, distributed randomly over an area. Source search can be carried out by robotic system in applications. However, for a single robot, the multisource search has been receiving relatively little attention compared to single-source search. For multisource task searching, a single robot has a high travel cost and is easy to trap a source which has been located before. In order to overcome these shortages, two multisource search algorithms inspired by the foraging behavior of Physarum polycephalum are proposed in this paper. First, a Physarum-inspired Strategy (PS) is designed based on the gradient climbing characteristic of Physarum polycephalum during foraging. The PS is simple and effective to let a mobile robot traverse all sources. Then, an extension algorithm named Physarum-inspired Decision-making Strategy (PDS) is proposed based on PS. Therein the synthetical field gradient model is established by introducing decision-making factor to obtain more accurate gradient information estimation. The PDS also introduces an obstacle avoidance model. Various simulation results obtained in the multisource environments show that the performance of PDS is better than other algorithms.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:3158156

DOI: 10.1155/2020/3158156

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