A Risk-Aware Obstacle Avoidance Path Planning Method for Unmanned Delivery Aerial Vehicles Under Urban Environment

Path planning plays an important role in autonomous flight in the complex urban environment to achieve goods delivery tasks. Besides dense obstacles, the wall effect near tall buildings brings great risks to the unmanned delivery aerial vehicle (UDAV). The objective of this work is to mitigate the risks and improve the safety and reliability of the UDAV. Motivated by this objective, a risk-aware obstacle avoidance path planning method for UDAV is proposed. First, the model of ducted fan-type UDAV is introduced. By analyzing its driving environment, different kinds of risks, including the wall effect, are constructed as an urban risk map, which reduces computational complexity by mapping them from three-dimensional space to two-dimensional planes. Then, a risk-aware improved artificial potential field (APF) path planning method is proposed, with a precognitive architecture, to plan a feasible path avoiding risks. The infeasible problems of the traditional APF method are solved by adding pre-decision-making instruction and continuous actions. Finally, scenarios are built to verify the effectiveness of the proposed method. Results show that the proposed method can plan a safe path in a complex urban environment. Compared to the planned path proposed by the popular particle swarm optimization method and the APF method, the risk index is decreased by 16.25% and 14.10%, respectively.