Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search
This addresses scalability and efficiency challenges in swarm robotics for search tasks, offering a novel decentralized approach with mathematical insights, though it is incremental in applying BO principles to this domain.
The paper tackled the problem of decentralized swarm robotic search for targets by developing Bayes-Swarm, a method based on batch Bayesian Optimization that decouples knowledge generation and task planning, demonstrating up to 76 times better efficiency than an exhaustive search baseline.
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.