This proposal is focused on enhancing and applying the trajectory generation algorithms originally developed at Caltech to a small free-flight unmanned Aero Utility Vehicle (AUV) that is under development with private funding. Our trajectory generation algorithms are based on a combination of nonlinear and optimal control theory, spline theory, and sequential quadratic programming. Furthermore, the algorithms have been successfully demonstrated on a tethered nonlinear flight control experiment at Caltech demonstrating the benefits of autonomous, real-time, nonlinear trajectory generation. We propose evaluating our algorithms with a simulation of the AUV under a variety of nonlinear trajectory generation and mission requirements. The proposed work would also provide the trajectory generation algorithms to our AUV concept for a hardware demonstration in Phase II. The fast, real-time trajectory generation algorithms and software under development by our team will enable future unmanned vehicles with futuristic capabilities. A global benefit of this proposal would be to enable future unmanned aircraft with autonomous proximity operation, autonomous border control, and strike aircraft like capabilities. This technology would empower exploration vehicles for oil companies and scientists as well as a new generation of search and rescue vehicles that can operate in extreme conditions. Ultimately, we see the potential to create the next revolution in human transportation with our technology.
Keywords: Trajectory Generation, Real-Time, Optimal Control, Path Planning
