论文标题
OA-BUG：在被拒绝的环境中用于群体机器人的嗅觉审计算法算法
OA-Bug: An Olfactory-Auditory Augmented Bug Algorithm for Swarm Robots in a Denied Environment
论文作者
论文摘要
在拒绝的环境中进行搜索对于群体机器人来说是一项挑战，因为不允许GNSS，映射，数据共享和中央处理的帮助。但是，使用嗅觉和听觉信号像动物一样合作可能是改善群体机器人协作的重要方法。在本文中，提出了一群自主机器人来探索拒绝环境的嗅觉审计增强算法算法（OA-BUG）。构建了一个模拟环境，以衡量OA-BUG的性能。搜索任务的覆盖范围可以使用OA-BUG达到96.93％，与类似的算法SGBA相比，该搜索任务可以显着改善。此外，在实际的群机器人上进行了实验，以证明OA-BUG的有效性。结果表明，OA-BUG可以在被拒绝的环境中提高群体机器人的性能。
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.
