Hybrid path planning and following of a quadrotor UAV based on deep reinforcement learning

Deep reinforcement learning(DRL)-based path planning algorithms have gained significant attention in recent years due to their end-to-end processing and robustness. Nevertheless, they face challenges in scenarios with long distances and dense obstacles because they only consider local environmental information. This paper presents a hybrid path planning and following approach that combines path planning based on soft actor-critic(SAC) with path following. Initially, a path is generated using the sampling-based method Adaptively Informed Trees (AIT*), and the path subsequently serves as tracking points for the planner. This approach guides the agent to move faster and more effectively toward the goal, and continuously updates the tracking point in real time. We conducted an experiment to evaluate the training process and performance of this hybrid path planning approach based on DRL while comparing it to the original DRL-based approach. The experimental results illustrate the superiority of the presented approach in both the training process and task performance.