Multi-Robot Active Sensing and Environmental Model Learning With Distributed Gaussian Process

• Published in: IEEE robotics and automation letters Vol. 5; no. 4; pp. 5905 - 5912
• Main Authors: Jang, Dohyun, Yoo, Jaehyun, Son, Clark Youngdong, Kim, Dabin, Kim, H. Jin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-10-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Collision avoidance; Computer simulation; Confidence intervals; Data models; distributed robot systems; Gaussian process; Gaussian processes; Kernel; Machine learning; Multi-robot systems; Multiagent systems; Multiple robots; networked robots; Protocols; Robot kinematics; Robot sensing systems; Robots; Spatial data; Statistical analysis; Topographic maps; Topographic surveys
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2020.3010456

This letter deals with the problem of multiple robots working together to explore and gather at the global maximum of the unknown field. Given noisy sensor measurements obtained at the location of robots with no prior knowledge about the environmental map, Gaussian process regression can be an efficient solution to construct a map that represents spatial information with confidence intervals. However, because the conventional Gaussian process algorithm operates in a centralized manner, it is difficult to process information coming from multiple distributed sensors in real-time. In this work, we propose a multi-robot exploration algorithm that deals with the following challenges: i) distributed environmental map construction using networked sensing platforms; ii) online learning using successive measurements suitable for a multi-robot team; iii) multi-agent coordination to discover the highest peak of an unknown environmental field with collision avoidance. We demonstrate the effectiveness of our algorithm via simulation and a topographic survey experiment with multiple UAVs.