Reactive Obstacle-Avoidance for Agile, Fixed-Wing, Unmanned Aerial Vehicles

Abstract

Agile, fixed-wing, aircraft have been proposed for diverse applications, due to their enhanced flight efficiency, compared to rotorcraft, and their superior maneuverability, relative to conventional, fixed-wing, aircraft. We present a novel, reactive, obstacle-avoidance algorithm that enables autonomous flight through unknown, cluttered environments using only on-board sensing and computation. The method selects a reference trajectory in real-time from a pre-computed library, based on goal location, instantaneous point cloud data, and the aircraft states. At each time-step, a cost is assigned to candidate trajectories that are collision-free and lead to the edge of the obstacle sensor’s field-of-view, with cost based on both distance to obstacles, and the goal. The lowest cost reference trajectory is then tracked. If all potential trajectories result in a collision, the aircraft has enough space to come to a stop, which theoretically guarantees collision-free flight. Our work demonstrates autonomous flight in unknown and unstructured environments using only on-board sensing (stereo camera, IMU, and GPS) and computation with an agile, fixed-wing, aircraft in both simulation and outdoor flight tests. During flight testing, the aircraft cumulatively flew 4.4km autonomously in outdoor environments with trees as obstacles with an average speed of 8.1ms−1 and a top speed of 14.4ms−1. To the best of our knowledge, ours is the first obstacle-avoidance algorithm suitable for agile, fixed-wing, aircraft that can theoretically guarantee collision-free flight and has been validated experimentally using only on-board sensing and computation in an unknown environment.