Abstract-Recent years have seen rapidly growing interest in the development of networks of multiple unmanned aerial vehicles (U.A.V.s), as aerial sensor networks for the purpose of coordinated monitoring, surveillance, and rapid emergency response. This has triggered a great deal of research in higher levels of planning and control, including collaborative sensing and exploration, synchronized motion planning, and formation or cooperative control. In this paper, we describe our recently developed experimental testbed at the University of Pennsylvania, which consists of multiple, fixed-wing UAVs. We describe the system architecture, software and hardware components, and overall system integration. We then derive high-fidelity models that are validated with hardware-in-theloop simulations and actual experiments. Our models are hybrid, capturing not only the physical dynamics of the aircraft, but also the mode switching logic that supervises lower level controllers. We conclude with a description of cooperative control experiments involving two fixed-wing UAVs.
Abstract. This paper describes the implementation of a decentralized architecture for autonomous teams of aerial and ground vehicles engaged in active perception. We provide a theoretical framework based on an established approach to the underlying sensor fusion problem [3]. This provides transparent integration of information from heterogeneous sources. The approach is extended to include an information-theoretic utility measure that captures the task objective and robot inter-dependencies. A distributed solution mechanism is employed to determine information maximizing trajectories and assignments subject to the constraints of individual vehicle and sensor sub-systems. This architecture enables the benefit of the complementary aerial and ground based vehicle and sensor capabilities to be realized. The approach is applied to missions involving searching for and tracking multiple ground targets. Experimental results for vehicles equipped with cameras are presented. These illustrate the impact of the team configuration on overall system performance.
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