## 2 Relate Work  
Traditional path-planning algorithms fall into two categories: global path planning and local path planning [4]. Global planning algorithms, such as Dijkstra, D*, A*, Rapidly-exploring Random Tree (RRT), and Genetic Algorithms (GA), can perform well in static environments but are inefficient when dealing with dynamic obstacles. In contrast, local path planning algorithms, such as Artificial Potential Field (APF), Dynamic Window Approach (DWA), and Ant Colony Optimization (ACO), can avoid obstacles in real-time with smooth paths but show restricted robustness and generality due to unpractical parameters. Researchers have tried to address the abovementioned issues from two perspectives. The first approach optimizes and combines traditional methods. [PERSON] et al. reduced the overall road cost for mobile robots by introducing road condition indicators into the evaluation function of the A* algorithm [5]. The A* algorithm has been combined with the Dynamic Window Approach (DWA) to enhance the dynamic obstacle avoidance capability of robots in complex environments [6; 7]. [PERSON] et al. optimized the DWA algorithm by introducing differential manifold tangent vectors and improving the evaluation function, enhancing the rationality and smoothness of path planning in environments with dense obstacles [8]. However, these improved methods still suffer from poor adaptability and reliance on initial parameter values in dynamic environments. The second approach adopts reinforcement learning for path planning. The Q-learning algorithm is a widely used classic reinforcement learning algorithm [9], thanks to its simplicity and quick converging speed. However, it suffers from the \"curse of dimensionality\" when dealing with complex environments. Deep reinforcement learning (DRL) combines deep learning with reinforcement learning, leveraging deep learning's capability to handle high-dimensional information to learn end-to-end strategies for complicated planning tasks. However, DRL-based path planning methods, such as DQN [10], double DQN [11], and dueling DQN [12], require transforming high-dimensional continuous actions into discrete action sequences, which may alter the structure of the action domain and hinder the completion of path planning tasks. [PERSON] et al. proposed the deep deterministic policy gradient (DDPG) algorithm [3], which combines both DQN and deterministic policy gradient (DPG) [13]. In the same report, the DDPG was validated with improved stability and reduced time compared to DQN. Despite its high stability and efficiency, DDPG fails to obtain an optimal motion strategy due to its reward function's lack of kinematic characteristics. The absence leads to redundant actions, slow responses to obstacles, and growing collision risk. Moreover, the algorithm gets trapped in local optima in complex environments, due to the lack of global information. To address these issues, [PERSON] et al. set multiple optimization objectives based on the longitudinal kinematic characteristics of vehicles to improve vehicles' following performance [14]. [PERSON] et al. optimized the DDPG reward function based on robot kinematics, effectively overcoming DDPG's drawbacks, like long training cycles and poor path planning quality [2]. [PERSON] et al. designed a new DDPG reward function based on vehicle kinematics and established an RDL-based speed control model. The presented model significantly improves autonomous driving safety, operational efficiency, and comfort [15]. On the other hand, to address the 'trapped in local optima' issue, [PERSON] et al. addressed the 'trapped in local optima' issue of the RL planner via local RDL-based hierarchical planning [16]. [PERSON] et al. introduced Sequential Linear Programming (SLP) as global guidance to improve DDPG, remarkably increasing the ratio of successful navigations in dynamic environments [17].