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

TL;DR

This paper introduces OA-Bug, a novel olfactory-auditory augmented algorithm for swarm robots to efficiently explore denied environments, significantly improving coverage compared to existing methods, validated through simulation and real-world experiments.

Contribution

The paper presents a new olfactory-auditory augmented bug algorithm (OA-Bug) that enhances swarm robot collaboration in denied environments, a novel approach compared to traditional methods.

Findings

Coverage of 96.93% achieved with OA-Bug

Significant improvement over SGBA algorithm

Validated through both simulation and real robot experiments

Abstract

Searching in a denied environment is challenging for swarm robots as no assistance from GNSS, mapping, data sharing, and central processing is allowed. However, using olfactory and auditory signals 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 can reach 96.93% using OA-Bug, which is significantly improved 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. Video: https://youtu.be/vj9cRiSm9eM.