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

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 effic...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2020-10, Vol.5 (4), p.5905-5912
Main Authors: Jang, Dohyun, Yoo, Jaehyun, Son, Clark Youngdong, Kim, Dabin, Kim, H. Jin
Format: Article
Language:English
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
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Summary: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.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2020.3010456