Maximum Likelihood Path Planning for Fast Aerial Maneuvers and Collision Avoidance 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 kconnected 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 0.20.3ms using a single CPU thread on a modem embedded computer. In experiments, it enables a lightweight UAV to fly at 10ms in a cluttered forest environment (see Fig. 1 as an example).

Fast Timeoptimal Avoidance of Moving Obstacles for HighSpeed MAV Flight In this work, we propose a method to efficiently compute smooth, timeoptimal trajectories for micro aerial vehicles (MAVs) evading a moving obstacle. Our approach first computes an ndimensional trajectory from the start to an arbitrary target state including position, velocity and acceleration. It respects input and stateconstraints and is thus dynamically feasible. The trajectory is then efficiently checked for collisions, exploiting the piecewise polynomial formulation. If collisions occur, viastates are inserted into the trajectory to circumvent the obstacle and still maintain timeoptimality. These viastates are described by position, velocity, and acceleration. The evaluation shows that the computational demands of the proposed method are minimal such that obstacle avoidance can begin within few milliseconds. Optimality of generated trajectories, combined with the ability for frequent online replanning from nonhover initial conditions, make the approach well suited for evasion of suddenly perceived obstacles during fast flight.