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Decentralized swarm robotic solutions to searching for targets that emit aspatially varying signal promise task parallelism, time efficiency, and faulttolerance. It is, however, challenging for swarm algorithms to offerscalability and efficiency, while preserving mathematical insights into theexhibited behavior. A new decentralized search method (called Bayes-Swarm),founded on batch Bayesian Optimization (BO) principles, is presented here toaddress these challenges. Unlike swarm heuristics approaches, Bayes-Swarmdecouples the knowledge generation and task planning process, thus preservinginsights into the emergent behavior. Key contributions lie in: 1) modelingknowledge extraction over trajectories, unlike in BO; 2) time-adaptivelybalancing exploration/exploitation and using an efficient local penalizationapproach to account for potential interactions among different robots' plannedsamples; and 3) presenting an asynchronous implementation of the algorithm.This algorithm is tested on case studies with bimodal and highly multimodalsignal distributions. Up to 76 times better efficiency is demonstrated comparedto an exhaustive search baseline. The benefits of exploitation/explorationbalancing, asynchronous planning, and local penalization, and scalability withswarm size, are also demonstrated.

Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search