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.

翻译：由于全球导航卫星系统、绘图、数据共享和中央处理不允许提供协助,在被拒绝的环境中搜索对群落机器人构成挑战。然而,使用嗅觉和听觉与动物一样合作可能是改善群落机器人合作的重要途径。在本文中,提议为一群自主机器人探索被否定的环境而设置一个自动机器人扩增的虫子算法(OA-Bug ) 。一个模拟环境是用来测量OA-Bug的性能的。使用OA-Bug的搜索任务覆盖率可以达到96.93%,与类似的算法(SGBA)相比,增幅最大,为40.55%。此外,对真正的群状机器人进行了实验,以证明OA-Bug的有效性。结果显示,OA-Bug可以在一个被否定的环境中改进群状机器人的性能。