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Open AccessArticle

Distributed Multi-Robot Information Gathering under Spatio-Temporal Inter-Robot Constraints

German Aerospace Centre (DLR), 82234 Oberpfaffenhofen, Germany
Australian Centre for Field Robotics (ACFR), Sydney, NSW 2006, Australia
School of Engineering, Universidad Pablo de Olavide (UPO), 41013 Seville, Spain
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Viseras, A.; Xu, Z.; Merino, L. Distributed Multi-Robot Cooperation for Information Gathering under Communication Constraints. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 21–25 May 2018.
Sensors 2020, 20(2), 484;
Received: 4 November 2019 / Revised: 17 December 2019 / Accepted: 28 December 2019 / Published: 15 January 2020
(This article belongs to the Special Issue Unmanned Aerial Systems and Sensor Networks)
Information gathering (IG) algorithms aim to intelligently select the mobile robotic sensor actions required to efficiently obtain an accurate reconstruction of a physical process, such as an occupancy map, a wind field, or a magnetic field. Recently, multiple IG algorithms that benefit from multi-robot cooperation have been proposed in the literature. Most of these algorithms employ discretization of the state and action spaces, which makes them computationally intractable for robotic systems with complex dynamics. Moreover, they cannot deal with inter-robot restrictions such as collision avoidance or communication constraints. This paper presents a novel approach for multi-robot information gathering (MR-IG) that tackles the two aforementioned restrictions: (i) discretization of robot’s state space, and (ii) dealing with inter-robot constraints. Here we propose an algorithm that employs: (i) an underlying model of the physical process of interest, (ii) sampling-based planners to plan paths in a continuous domain, and (iii) a distributed decision-making algorithm to enable multi-robot coordination. In particular, we use the max-sum algorithm for distributed decision-making by defining an information-theoretic utility function. This function maximizes IG, while fulfilling inter-robot communication and collision avoidance constraints. We validate our proposed approach in simulations, and in a field experiment where three quadcopters explore a simulated wind field. Results demonstrate the effectiveness and scalability with respect to the number of robots of our approach. View Full-Text
Keywords: robotics; distributed multi-agent systems; information gathering; Gaussian processes robotics; distributed multi-agent systems; information gathering; Gaussian processes
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Viseras, A.; Xu, Z.; Merino, L. Distributed Multi-Robot Information Gathering under Spatio-Temporal Inter-Robot Constraints. Sensors 2020, 20, 484.

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