###### Abstract
developed the DTDDQN network, which combined the Averaged-DQN and DoubleDQN algorithms while leveraging prior experience to reduce the discrepancy between estimated and true values [21]. [PERSON] et al. merged the DQN algorithm with LSTM networks, thereby allowing the LSTM network to learn and retain state information from moments before and after time t. This memory chain facilitates faster decision-making [22]. [PERSON] et al. proposed preferential empirical replay, where samples were selected from the replay pool based on their Time Difference error (TD-error) values as probabilities. This approach accelerates convergence [23]. Building upon priority experience replay, [PERSON] introduced an experience replay mechanism based on the entropy of state information to ensure sample diversity [24]. [PERSON] et al. devised a multi-path planning algorithm for autonomous vehicles based on the DQN, enabling path planning in multiple environments [25]. [PERSON] et al. applied the DQN algorithm to path planning, with the neural network taking environmental information acquired by the vehicle's sensors as an input and outputting the state-action Q-value, achieving end-to-end mapping [26]. [PERSON] et al. applied the Double DQN network to path planning in unknown environments. They continuously adjusted the target and starting positions during training to enhance the algorithm's generalization ability in diverse location environments [27]. [PERSON] proposed the deep deterministic policy gradient (DDPG), a DRL algorithm for automatic driving simulation. DDPG models the path, following the control, reward function, actor-network, and critic network [28]. [PERSON] introduced the concept of maximum comfortable speed to represent the vertical ride comfort of roads. They designed a DRL algorithm to learn comfortable and energy-efficient speed control strategies [29]. [PERSON] et al. proposed a probabilistic model-based risk assessment method for evaluating driving risks using location uncertainty and distance-based safety indicators. They also developed a risk perception decision algorithm that utilized DRL to find strategies with the least expected risk. The proposed approach was evaluated using the CARLA simulator in two scenarios involving static obstacles and dynamically moving vehicles. The results demonstrate that the method can generate safe driving strategies which outperform existing approaches [30]. This paper discusses the use of a Hardware-In-Loop (HIL) simulation interface for wheeled mobile robots [31]. Traditional global path planning suffers from too many turning points and is not smooth, which is not conducive to unmanned vehicles [32]; meanwhile, local path planning in dynamic environments, with the help of the global path as a direction, also requires dynamic obstacle avoidance and short-range path planning in real time [33]. Traditional path planning algorithms such as the A* algorithm developed based on [PERSON]'s algorithm [34], the artificial potential field algorithm, DWA algorithm, etc., have certain features and advantages for the actual unmanned vehicle path planning, but there are still some shortcomings, such as the traditional DWA algorithm requires that the vehicle can rotate in place to achieve obstacle avoidance and path replanning for the traditional DWA algorithm to carry out the local path planning, which is suitable for omnidirectional mobile unmanned vehicles, and does not apply to the two-wheel-drive unmanned vehicles. The traditional artificial potential field algorithm suffers from target unreachability and local minimum problems, for example, the A* algorithm suffers from planning paths that are not smooth and have too many turns. The common shortcoming of traditional path planning algorithms based on raster maps, sampling-based path planning algorithms, and bionic heuristic-based path planning algorithms is that they all need to build a certain map to carry out the planning, which is a big limitation for unmanned vehicles exploring the path planning in unknown environments.