Self-triggered collision avoidance control for multi-vehicle systems

The implementation of collision detection mechanisms on-board of unmanned vehicles typically relies on digital platforms and shared communication networks. The discrete nature of these technologies does not only limit the frequency at which nearby obstacles and other vehicles are detected, but it also raises the demand for a better allocation of the vehicle´s computational and communication resources. For instance, it is natural and convenient to adapt the frequency at which other vehicles are observed based on their distance and collision risk, a notion that relates to topics in minimum attention control and self-triggered control. In this paper, we report on a cooperative, decentralized self-triggered avoidance control law for an arbitrary large group of unmanned vehicles that reduces the attention and sampling frequency of other vehicles´ position. The proposed control strategy guarantees the safe coordination of all vehicles at all times while reducing the sampling frequency of other vehicles´ position based on their last available sample. We show that the sampling intervals are positive and lower bounded, which is critical for digital implementation. A simulation example is used to illustrate the performance of the avoidance control strategy.