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This paper discusses the development and testing of a unique testbed consisting of a fleet of eight autonomous unmanned aerial vehicles (UAVs) that was designed as a platform for evaluating autonomous coordination and control algorithms. Future UAV teams will have to autonomously demonstrate cooperative behaviors in dynamic and uncertain environments, and this testbed can be used to compare various control approaches to accomplish these coordinated missions. A hierarchical configuration of task assignment, trajectory design, and low-level, waypoint following, are used in a receding horizon framework to control the UAV team. Numerous trajectory optimization and team coordination algorithms have recently been developed to execute these UAV missions. This paper highlights several of these algorithms and presents typical results for representative experiments. These demonstrations of the high-level planning algorithms on scaled vehicles operating in uncertain and dynamic environments represent key steps towards transitioning them to future UAV missions. I.
How et al. (Sat,) studied this question.