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We present the first hardware-in-the-loop evolutionary optimization on an ornithopter. Our experiments demonstrate the feasibility of evolving flight through genetic algorithms and adaptable hardware, without the requirement for a thorough knowledge of the aerodynamics of flapping flight. In this research we successfully optimized forward velocity and basic efficiency on an actual hardware ornithopter. The ornithopter was flown integrated to a "whirling-arm" test apparatus, allowing lengthy experimental flights without the risk of crashing. Flapping rate and tail position were controlled by an evolutionary algorithm with feedback of forward velocity and motor power. The system evolved an unexpected optimal configuration. This paper discusses the development of the test apparatus and experimental results from the initial phase of research.

This article presents a framework for searching, detecting, and tracking an object of interest with a small unmanned aerial vehicle (sUAV). The vehicle is given an area to search for an object of interest. Once the object is detected, the sUAV follows the target while maintaining a fixed distance and centered on its image plane. This paper describes an architecture for integrating autonomy capabilities for search and track for sUAVs. The system has been implemented in the Robot Operating System (ROS) framework using the Parrot ARDrone platform. In this paper we present our technical approach, the system architecture and demonstrate the principles of the approach in a simulation environment and indoor flight tests.

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