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OA-Bug: An Olfactory-Auditory Augmented Bug Algorithm for Swarm Robots in a Denied Environment

2022-09-28 11:29:28
Siqi Tan, Xiaoya Zhang, Jingyao Li, Ruitao Jing, Mufan Zhao, Yang Liu, Quan Quan
arXiv_RO
     
Abstract

Searching in a denied environment is challenging for swarm robots as no assistance from GNSS, mapping, data sharing, and central processing are allowed. However, using olfactory and auditory to cooperate like animals could be an important way to improve the collaboration of swarm robots. In this paper, an Olfactory-Auditory augmented Bug algorithm (OA-Bug) is proposed for a swarm of autonomous robots to explore a denied environment. A simulation environment is built to measure the performance of OA-Bug. The coverage of the search task using OA-Bug can reach 96.93%, with the most significant improvement of 40.55% compared with a similar algorithm, SGBA. Furthermore, experiments are conducted on real swarm robots to prove the validity of OA-Bug. Results show that OA-Bug can improve the performance of swarm robots in a denied environment.

Abstract (translated)

URL

https://arxiv.org/abs/2209.14007

PDF

https://arxiv.org/pdf/2209.14007.pdf

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