Distributed multi-robot formation control in dynamic environments

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 co...

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Bibliographic Details
Published in:Autonomous robots Vol. 43; no. 5; pp. 1079 - 1100
Main Authors: Alonso-Mora, Javier, Montijano, Eduardo, Nägeli, Tobias, Hilliges, Otmar, Schwager, Mac, Rus, Daniela
Format: Journal Article
Language:English
Published: New York Springer US 15.06.2019
Springer Nature B.V
Subjects:
Artificial Intelligence
Collision avoidance
Computational geometry
Computer Imaging
Computer simulation
Control
Convexity
Engineering
Hulls
Mathematical programming
Mechatronics
Moving obstacles
Multiple robots
Optimization
Parameters
Pattern Recognition and Graphics
Reconfiguration
Robot control
Robotics
Robotics and Automation
Robots
Rotary wing aircraft
Visibility
Vision
Distributed robotics
Dynamic environments
Formation control
Unmanned aerial vehicles
Micro air vehicles
Multi-robot systems
Collision avoidance
Drones
ISSN:0929-5593, 1573-7527
Online Access:Get full text
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Summary: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.
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ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-018-9783-9