Conflict-free navigation in unknown urban environments

This paper presents an autonomous exploration method in an unknown environment that uses model predictive control (MPC)-based obstacle avoidance with local map building by onboard sensing. An onboard laser scanner is used to build an online map of obstacles around the vehicle with outstanding accuracy. This local map is combined with a real-time MPC algorithm that generates a safe vehicle path, using a cost function that penalizes the proximity to the nearest obstacle. The adjusted trajectory is then sent to a position tracking layer in the hierarchical unmanned aerial vehicle (UAV) avionics architecture. In a series of experiments using a Berkeley UAV, the proposed approach successfully guided the vehicle safely through the urban canyon