Autonomous Exploration in Unknown Urban Environments for Unmanned Aerial Vehicles

§In this paper, we present an autonomous exploration method for unmanned aerial vehicles in unknown urban environment. We address two major aspects of explorationgathering information about the surroundings and avoiding obstacles in the flight path- by building local obstacle maps and solving for confli ct-free trajectory using model predictive control (MPC) framework. For obstacle sensing, an onboard laser scanner is used to detect nearby objects around the vehicle. An MPC algorithm with a cost function that penalizes the proximity to the nearest obstacle replans the fligh t path in real-time. The adjusted trajectory is sent to the position tracking layer in the UAV a vionics. The proposed approach is implemented on Berkeley rotorcraft UAVs and successfully tested in a series of flights in urban obstacle setup.