P-CAP: Pre-computed Alternative Paths to Enable Aggressive Aerial Maneuvers in Cluttered Environments

Abstract

We propose a novel method to enable fast autonomous flight in cluttered 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. As the vehicle progresses, modification of the graph with data from onboard sensors is expensive as is search on the graph, especially if the paths must be kino-dynamically feasible. We suggest that computation needed to find safe paths during fast flight can be greatly reduced if we precompute and carefully arrange a dense set of alternative paths before the flight. Any prior map information can be used to prune the alternative paths to come up with a data structure that enables very fast online computation to deal with obstacles that are not on the map but only detected by onboard sensors. To test this idea, we have conducted a large number of flight experiments in structured (large industrial facilities) and unstructured (forests-like) environments. We show that even in the most unstructured environments, this method enables flight at a speed up to 10m/s while avoiding obstacles detected from onboard sensors.