Abstract
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.
Highlights
Multi-vehicle cooperative missions have become the focus of many researches, because they allow for the completion of tasks in a faster, safer, and more efficient way than single vehicle missions
Some of the techniques for collision avoidance found in the literature include (i) trajectory planning, where agents’ trajectories are planned to create collision-free paths [6,7,8]; (ii) velocity obstacle (VO) methods, where the set of velocities that will result in collisions is defined, and the vehicle’s velocity is adjusted [9,10,11]; (iii) machine learning based methods, such as deep reinforcement learning [12,13]; (iv) and game theory [14,15,16]
Consider a cooperative mission involving N vehicles, with NL vehicles elected as leaders
Summary
Multi-vehicle cooperative missions have become the focus of many researches, because they allow for the completion of tasks in a faster, safer, and more efficient way than single vehicle missions. In this work it is assumed that the coordination variables and the desired mission pace are known by all of the agents and to a subset of leaders Motivated by these ideas, we build on previous results on time-coordination [26,27,28,29], and propose a Lyapunov-based method to the cooperative collision avoidance problem. Pi(t) is the ith trajectory generated at the motion planning level With this definition, the virtual time is a variable, which to the clock time, can be stretched or compressed to adjust the progression of the virtual target along the geometric path pi(·), in order to achieve some objectives, e.g., coordination or collision avoidance, as we will see later. For this reason, when deriving control laws for γi(t), the bounds in (6) will be taken into consideration
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