Fully Distributed Informative Planning for Environmental Learning with Multi-Robot Systems
This addresses the challenge of scalable and efficient exploration for multi-robot systems in environmental learning tasks, though it appears incremental as it combines existing distributed methods.
The paper tackles the problem of cooperative environmental learning and path planning for multi-robot systems by proposing a fully distributed algorithm that divides the process into environmental learning using distributed Gaussian processes and path planning using distributed Monte Carlo tree search, resulting in a scalable system validated through simulations and real-world-dataset-based tests.
This paper proposes a cooperative environmental learning algorithm working in a fully distributed manner. A multi-robot system is more effective for exploration tasks than a single robot, but it involves the following challenges: 1) online distributed learning of environmental map using multiple robots; 2) generation of safe and efficient exploration path based on the learned map; and 3) maintenance of the scalability with respect to the number of robots. To this end, we divide the entire process into two stages of environmental learning and path planning. Distributed algorithms are applied in each stage and combined through communication between adjacent robots. The environmental learning algorithm uses a distributed Gaussian process, and the path planning algorithm uses a distributed Monte Carlo tree search. As a result, we build a scalable system without the constraint on the number of robots. Simulation results demonstrate the performance and scalability of the proposed system. Moreover, a real-world-dataset-based simulation validates the utility of our algorithm in a more realistic scenario.