Falco: Fast likelihood-based collision avoidance with extension to human-guided navigation

We propose a planning method to enable fast autonomous flight in cluttered environments. Typically, autonomous navigation through a complex environment requires a continuous search on a graph generated by a k-connected grid or a probabilistic scheme. As the vehicle travels, updating the graph with data from onboard sensors is expensive as is the search on the graph especially if the paths must be kinodynamically feasible. We propose to avoid the online search to reduce the computational complexity. Our method models the environment differently in two separate regions. Obstacles are considered to be deterministically known within the sensor range and probabilistically known beyond the sensor range. Instead of searching for the path with the lowest cost (typically the shortest path), the method maximizes the likelihood to reach the goal in determining the immediate next step for navigation. With such a problem formulation, the online method realized by a trajectory library can determine a path within 0.2-0.3 ms using a single central processing unit thread on a modem embedded computer. The method supports two configurations working with and without a prior map. Both configurations can be used to plan toward a goal point. Further, the later can allow human guidance for the navigation through a directional input. In experiments, it enables a lightweight unmanned aerial vehicle to fly at 10 m/s in a cluttered forest environment (see Figure 1 as an example).