  Decentralized swarm robotic solutions to searching for targets that emit a
spatially varying signal promise task parallelism, time efficiency, and fault
tolerance. It is, however, challenging for swarm algorithms to offer
scalability and efficiency, while preserving mathematical insights into the
exhibited behavior. A new decentralized search method (called Bayes-Swarm),
founded on batch Bayesian Optimization (BO) principles, is presented here to
address these challenges. Unlike swarm heuristics approaches, Bayes-Swarm
decouples the knowledge generation and task planning process, thus preserving
insights into the emergent behavior. Key contributions lie in: 1) modeling
knowledge extraction over trajectories, unlike in BO; 2) time-adaptively
balancing exploration/exploitation and using an efficient local penalization
approach to account for potential interactions among different robots' planned
samples; and 3) presenting an asynchronous implementation of the algorithm.
This algorithm is tested on case studies with bimodal and highly multimodal
signal distributions. Up to 76 times better efficiency is demonstrated compared
to an exhaustive search baseline. The benefits of exploitation/exploration
balancing, asynchronous planning, and local penalization, and scalability with
swarm size, are also demonstrated.
