###### Abstract  
Autonomous vehicles can reduce labor power during cargo transportation, and then improve transportation efficiency, for example, the automated guided vehicle (AGV) in the warehouse can improve the operation efficiency. To overcome the limitations of traditional path planning algorithms in unknown environments, such as reliance on high-precision maps, lack of generalization ability, and obstacle avoidance capability, this study focuses on investigating the Deep Q-Network and its derivative algorithm to enhance network and algorithm structures. A new algorithm named APF-D3 QNPER is proposed, which combines the action output method of artificial potential field (APF) with the Dueling Double Deep Q Network algorithm, and experience sample rewards are considered in the experience playback portion of the traditional Deep Reinforcement Learning (DRL) algorithm, which enhances the convergence ability of the traditional DRL algorithm. A long short-term memory (LSTM) network is added to the state feature extraction network part to improve its adaptability in unknown environments and enhance its spatiotemporal sensitivity to the environment. The APF-D3 QNPER algorithm is compared with mainstream deep reinforcement learning algorithms and traditional path planning algorithms using a robot operating system and the Gazebo simulation platform by conducting experiments. The results demonstrate that the APF-D3 QNPER algorithm exhibits excellent generalization abilities in the simulation environment, and the convergence speed, the loss value, the path planning time, and the path planning length of the APF-D3 QNPER algorithm are all less than for other algorithms in diverse scenarios. deep reinforcement learning; autonomous vehicle; path planning; DQN algorithm 202313100566 20231  
such as Nuro, Marble, Amazon (with its Scout autonomous logistics vehicle), and Einride (with its autonomous electric logistics vehicle in Sweden), entered the market and began regular operations in the autonomous logistics sector. In China, logistics platforms, such as Jingdong, Meituan, and Cainiao, have implemented autonomous vehicles for operational testing on university campuses and closed parks. Startups, such as New Stone and Wisdom Walker, have also ventured into the autonomous delivery field. Companies, such as Shunfeng and Jingdong, have been involved in the research and development of drones and have conducted trial operations. Suning's electric autonomous vehicle, Wolong No. 1, is now in normal operation. Cainiao Logistics is actively conducting experiments related to autonomous urban logistics delivery. The application of autonomous driving technology in the logistics industry undoubtedly brings opportunities and challenges to the field. Traditional path planning for autonomous vehicles can be categorized into local and global planning algorithms based on the scope of required environmental information [3]. Global path planning involves acquiring environmental information in advance and generating a map. The algorithm plans the path based on the starting and target poses on the map, as well as the positions of static and dynamic obstacles. Meanwhile, local path planning focuses on the immediate surroundings of the vehicle, utilizing sensor information to avoid static obstacles, predict the trajectory of sudden dynamic obstacles, and find feasible paths. In dynamic environments, autonomous vehicles often require global and local path planning. They use the global path as a general direction while dynamically avoiding obstacles and performing short-distance path planning with the assistance of local path planning [4]. Based on algorithmic principles, path planning methods can be classified into raster map-based, sampling-based, and bionic heuristic-based approaches [5; 6; 7; 8; 9; 10; 11]. Map-based methods include algorithms, such as [PERSON]'s algorithm, A*, and D*. Sampling-based methods encompass the RRT algorithm, PRM algorithm, and APF method.