TY - JOUR
T1 - Time Coordination and Collision Avoidance Using Leader-Follower Strategies in Multi-Vehicle Missions
AU - Tabasso, Camilla
AU - Cichella, Venanzio
AU - Mehdi, Syed Bilal
AU - Marinho, Thiago
AU - Hovakimyan, Naira
N1 - Funding Information:
This work was supported by ONR, AFOSR, NSF and NASA.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/3
Y1 - 2021/3
N2 - In recent years, the increasing popularity of multi-vehicle missions has been accompanied by a growing interest in the development of control strategies to ensure safety in these scenarios. In this work, we propose a control framework for coordination and collision avoidance in cooperative multi-vehicle missions based on a speed adjustment approach. The overall problem is decoupled in a coordination problem, in order to ensure coordination and inter-vehicle safety among the agents, and a collision-avoidance problem to guarantee the avoidance of non-cooperative moving obstacles. We model the network over which the cooperative vehicles communicate using tools from graph theory, and take communication losses and time delays into account. Finally, through a rigorous Lyapunov analysis, we provide performance bounds and demonstrate the efficacy of the algorithms with numerical and experimental results.
AB - In recent years, the increasing popularity of multi-vehicle missions has been accompanied by a growing interest in the development of control strategies to ensure safety in these scenarios. In this work, we propose a control framework for coordination and collision avoidance in cooperative multi-vehicle missions based on a speed adjustment approach. The overall problem is decoupled in a coordination problem, in order to ensure coordination and inter-vehicle safety among the agents, and a collision-avoidance problem to guarantee the avoidance of non-cooperative moving obstacles. We model the network over which the cooperative vehicles communicate using tools from graph theory, and take communication losses and time delays into account. Finally, through a rigorous Lyapunov analysis, we provide performance bounds and demonstrate the efficacy of the algorithms with numerical and experimental results.
KW - Autonomous systems
KW - Collision avoidance
KW - Multi-vehicle coordination
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U2 - 10.3390/robotics10010034
DO - 10.3390/robotics10010034
M3 - Article
SN - 2218-6581
VL - 10
SP - 1
EP - 23
JO - Robotics
JF - Robotics
IS - 1
M1 - 34
ER -