Paper Info
Title
Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search
Abstract
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.
Year
DOI
Venue
2019
10.1109/MRS.2019.8901084
2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)
Keywords
Field
DocType
Swarm Robotic Search,Informative Path Planning,Bayesian Search,Gaussian Process,Asynchronous
Motion planning,Asynchronous communication,Brute-force search,Swarm behaviour,Task parallelism,Computer science,Bayesian search theory,Heuristics,Distributed computing,Scalability
Conference
ISBN
Citations 
PageRank 
978-1-7281-2877-1
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Payam Ghassemi101.69
Souma Chowdhury277.63