  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.
