Distributed multi-robot formation control in dynamic environments
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10514_2018_Article_9783.pdf
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Author(s)
Montijano, Eduardo
Nägeli, Tobias
Hilliges, Otmar
Alonso Mora, Javier
Schwager, Mac
Rus, Daniela L
Date Issued
July 2018
Journal
Autonomous Robots
Publisher
Springer US
Citation
Alonso-Mora, Javier, Eduardo Montijano, Tobias Nägeli, Otmar Hilliges, Mac Schwager, and Daniela Rus. “Distributed Multi-Robot Formation Control in Dynamic Environments.” Autonomous Robots (July 20, 2018).
Version
Final published version
Abstract
This paper presents a distributed method for formation control of a homogeneous team of aerial or ground mobile robots navigating in environments with static and dynamic obstacles. Each robot in the team has a finite communication and visibility radius and shares information with its neighbors to coordinate. Our approach leverages both constrained optimization and multi-robot consensus to compute the parameters of the multi-robot formation. This ensures that the robots make progress and avoid collisions with static and moving obstacles. In particular, via distributed consensus, the robots compute (a) the convex hull of the robot positions, (b) the desired direction of movement and (c) a large convex region embedded in the four dimensional position-time free space. The robots then compute, via sequential convex programming, the locally optimal parameters for the formation to remain within the convex neighborhood of the robots. The method allows for reconfiguration. Each robot then navigates towards its assigned position in the target collision-free formation via an individual controller that accounts for its dynamics. This approach is efficient and scalable with the number of robots. We present an extensive evaluation of the communication requirements and verify the method in simulations with up to sixteen quadrotors. Lastly, we present experiments with four real quadrotors flying in formation in an environment with one moving human.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1007/s10514-018-9783-9
