Decentralized Multi-Robot Information Gathering From Unknown Spatial Fields
- Resource Type
- Periodical
- Authors
- Newaz, A.A.R.; Alsayegh, M.; Alam, T.; Bobadilla, L.
- Source
- IEEE Robotics and Automation Letters IEEE Robot. Autom. Lett. Robotics and Automation Letters, IEEE. 8(5):3070-3077 May, 2023
- Subject
- Robotics and Control Systems
Computing and Processing
Components, Circuits, Devices and Systems
Robots
Robot sensing systems
Robot kinematics
Planning
Density functional theory
Bayes methods
Optimization
Asynchronous multi-robot information gathering
Bayesian optimization
decentralized motion planning
distributed control
nonholonomic robots
spatial fields
- Language
- ISSN
- 2377-3766
2377-3774
We present an incremental scalable motion planning algorithm for finding maximally informative trajectories for decentralized mobile robots. These robots are deployed to observe an unknown spatial field, where the informativeness of observations is specified as a density function. Existing works that are typically restricted to discrete domains and synchronous planning often scale poorly depending on the size of the problem. Our goal is to design a distributed control law in continuous domains and an asynchronous communication strategy to guide a team of cooperative robots to visit the most informative locations within a limited mission duration. Our proposed Asynchronous Information Gathering with Bayesian Optimization (AsyncIGBO) algorithm extends ideas from asynchronous Bayesian Optimization (BO) to efficiently sample from a density function. It then combines them with decentralized reactive motion planning techniques to achieve efficient multi-robot information gathering activities. We provide a theoretical justification for our algorithm by deriving an asymptotic no-regret analysis with respect to a known spatial field. Our proposed algorithm is extensively validated through simulation and real-world experiment results with multiple robots.