Before revision:  We present an algorithm called Noise-Aware Thompson Sampling (NATS) that addresses these issues for multiple ground-based robot agents performing active search considering two sources of sensory information from monocular optical imagery and sonar tracking. NATS utilizes communications between robot agents in a decentralized manner that is robust to intermittent loss of communication links. Additionally, it takes into account object detection uncertainty from depth as well as environmental occlusions  . Using simulation results, we show that NATS significantly outperforms existing methods such as information-greedy policies or exhaustive search.
After revision:  We present an algorithm called Noise-Aware Thompson Sampling (NATS) that addresses these issues for multiple ground-based robot agents performing active search considering two sources of sensory information from monocular optical imagery and depth maps. By utilizing Thompson Sampling, NATS allows for decentralized coordination among multiple agents. NATS also considers object detection uncertainty from depth as well as environmental occlusions . Using simulation results, we show that NATS significantly outperforms existing methods such as information-greedy policies or exhaustive search.