OA-Bug: 嗅覚・聴覚を活用した拒否環境下での群ロボット探索アルゴリズム
OA-Bug: An Olfactory-Auditory Augmented Bug Algorithm for Swarm Robots in a Denied Environment
GNSSや地図・通信が使えない拒否環境で、動物のように嗅覚と聴覚の信号を利用して群ロボットが協調探索するBugアルゴリズムを提案し、シミュレーションと実機実験で有効性を示した。
著者: Siqi Tan, Xiaoya Zhang, Jingyao Li, Ruitao Jing, Mufan Zhao, Yang Liu, Quan Quan
分類: cs.RO, cs.MA
原文アブストラクト
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.
