Path Planning with Improved Dueling DQN Algorithm for UAVs in Unknown Dynamic Environment

Unmanned Aerial Vehicles (UAVs) have been widely used in emergency rescue because of their small size and low cost, which can effectively reduce casualties and improve rescue efficiency. However, for UAV collision avoidance path planning in an unknown dynamic environment is a relatively difficult optimization problem. On the one hand, it is difficult for traditional algorithms to adapt to the huge state space generated by three-dimensional environment, which makes convergence difficult. On the other hand, the dynamic changing environment will affect the flight safety of UAVs. In this paper, an Improved Memory Pool Dueling Deep Q Network algorithm (IM-Dueling DQN) is proposed to optimize obstacle avoidance and path planning. Combined with the state and action space, a comprehensive reward function is designed to speed up the convergence of the algorithm and improve the generalization ability of the UAV to the environment. Experimental simulations show that the algorithm has good robustness in an unknown dynamic environment, and can avoid all obstacles and plan a safe navigation route.