Autonomous Emergency Landing of a Helicopter: Motion Planning with Hard Time-Constraints

Abstract

Engine malfunctions during helicopter flight poses a large risk to pilot and crew. Without a quick and coordinated reaction, such situations lead to a complete loss of control. An autonomous landing system is capable of reacting quickly to regain control, however current emergency landing methods focus only on the offline generation of dynamically feasible trajectories while ignoring the more severe constraints faced while autonomously landing a real helicopter during an unplanned engine failure. We address the problem of autonomously landing a helicopter while considering a realistic context: hard time-constraints, challenging terrain, sensor limitations and availability of pilot contextual knowledge. We designed a planning system that deals with all these factors by being able to compute alternate routes (AR) in a rapid fashion. This paper presents an algorithm, RRT*-AR, building upon the optimal sampling-based algorithm RRT* to generate AR in realtime while maintaining optimality guarantees and examines its performance for simulated failures occurring in mountainous terrain. After over 4500 trials, RRT*-AR outperformed RRT* by providing the human 280% more options 67% faster on average. As a result, it provides a much wider safety margin for unaccounted disturbances, and a more secure environment for a pilot.