P-CAL: Pre-computed Alternative Lanes for Aggressive Aerial Collision Avoidance

Abstract

We here address the issue of air vehicles flying autonomously at a high speed in complex environments. Typically, autonomous navigation through a complex environment requires a continuous heuristic search on a graph generated by a k-connected grid or a probabilistic scheme. The process is expensive especially if the paths must be kino-dynamically feasible. Aimed at tackling the problem from a different angle, we consider the case that the environment is mostly known from a prior map. The proposed method suggests the computation needed to find safe paths during fast flight can be greatly reduced if we pre-compute and carefully arrange a set of alternative paths before the flight. During the navigation, the vehicle selects a pre-computed path to navigate without the need to generate a new path. The result is that majority of the processing is migrated to offline path generation. Effectively, the onboard computation is significantly reduced, taking < 3% of a CPU thread on a modern embedded computer. In experiments, it enables a lightweight aerial vehicle to maneuver aggressively through a cluttered forest environment at 10m/s.