## 1 Introduction  
Mobile robots have been employed extensively worldwide in recent years because of their independent and adaptable qualities, which are widely used in industrial production, life services, medical and health services, and national defense-related science and technology. Path planning has emerged as the main issue that requires immediate attention due to the growing popularity of mobile robots [1, 2, 3]. Depending on the different working circumstances, there are two forms of route planning: global static path planning and local dynamic path planning. Only the route planning issue related to mobile robots in static surroundings may be resolved by global static path planning. The Dijkstra algorithm, the A* algorithm, the rapid random tree search algorithm, and others are examples of common techniques [4; 5]. [PERSON]'s algorithm and A* algorithm are both used in heuristic search methods in the field of path planning [6; 7].The traditional A* algorithm has been frequently studied due to its defects, such as node duplication and a large number of inflection points [8; 9; 10]. Manhattan-Euclid distance hybrid technology improves the heuristic function, reduces the search time, and improves the search efficiency of the basic A* algorithm [11]. In order to improve search efficiency, the bidirectional algorithm searches in both positive and negative directions at the same time [12]. Dynamic window technique is a local dynamic path planning method. Although the path is quite smooth, it is susceptible to local optimization, and the global optimal path does not allow it to reach the ideal position [13; 14; 15]. Although genetic algorithm is reliable and easy to integrate with other algorithms, its search time is long, and its convergence speed is slow [16; 17; 18]. [PERSON] and his colleagues proposed a path planning method based on the Bezier curve and Modified Genetic Algorithm (MGA) to solve the problems of traditional path planning algorithms in dynamic environments. This method combines the smoothness of Bezier curve with the global search ability of genetic algorithm and can plan the optimal path for a mobile robot in dynamic environment [19]. A hybrid approach that combines the artificial potential field technique with the A* algorithm to plan routes in a dynamic environment was presented in the literature [20; 21; 22; 23]. However, overall efficiency was reduced by the artificial potential field method's inability to more effectively design the locally optimum route [24; 25]. Reinforcement learning [26; 27] was proposed by American scholars [PERSON] and [PERSON] in 1998. The advantage of a reinforcement learning algorithm in path planning is that it can learn the optimal strategy without knowing the details of the environment, and it can also adapt to changes in the dynamic environment. However, when the environment model is too large, slow algorithm convergence as well as the problem of dimensionality disaster, which is the non-optimal solution caused by no environment model, will occur because the model needs to calculate the maximum cumulative reward value. Many scholars at home and abroad have improved the reinforcement learning algorithm and successfully applied it to the path planning of mobile robots. [PERSON] et al. [28] proposed an asynchronous reinforcement learning algorithm based on parallel particle swarm optimization, applying the particle swarm optimization algorithm to the asynchronous reinforcement learning algorithm to find the optimal solution of the algorithm strategy. [PERSON] et al. [29] proposed a whale optimization algorithm, which can effectively initialize the Q-table and dynamically adjust greedy strategy values through nonlinear functions, thus greatly improving the convergence speed and route planning accuracy of the algorithm. Deep reinforcement learning [30] is a method that combines deep learning and reinforcement learning to solve complex decision problems, so it can be used to solve the path planning problem of mobile robots in unknown complex environments.