Coordinated Vision-Based Tracking by Multiple Unmanned Vehicles

We address the problem of coordinated vision-based tracking of a moving target using multiple unmanned vehicles that exchange information over a supporting time-varying network. The objective of this work is to formulate decentralized control algorithms that enable multiple vehicles to follow the target while coordinating their phase separation. A typical scenario involves multiple unmanned aerial vehicles that are required to monitor a moving ground object (target tracking) while maintaining a desired inter-vehicle separation (coordination). To solve the vision-based tracking problem, the yaw rate of each vehicle is used as the control input, while the speeds of the vehicles are adjusted to achieve coordination. It is assumed that the vehicles are equipped with an internal autopilot, which is able to track yaw rate and speed commands. The performance of the coordinated vision-based tracking algorithm is evaluated as a function of the target's velocity, tracking performance of the onboard autopilot, and the quality of service of the communication network.