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UAV path planning with kinematic constraints based on deep reinforcement learning

With the rapid development of UAVs, the application of UAVs in civil and military fields has greatly been expanded. UAVs are often used to perform tasks in hazardous areas because of its flexibility. Although many different path planning algorithms have been proposed to find optimal or near-optimal collision-free paths for UAVs, most of them either do not consider dynamic obstacle avoidance or ignore nonlinear kinematic characteristics of UAVs, which is impractical. In this paper, a Deep Reinforcement Learning (DRL) based path planning algorithm with the nonlinear kinematic constraints is proposed. For static obstacles and task points, the reward function is designed and DRL is used to generate the flight path for multiple task points. When UAV detects a dynamic obstacle, we assume that it transmits coordinates of the obstacle to a computer center to regenerate the remaining path. Wilson Confidence Interval (WCI) is then used to predict time taken by computer center to recreate the new path. Simulation results validate the efficiency of the proposed algorithm.