## 2 Related Work
In the application of path planning, [PERSON] et al. [32] aimed at the path planning problem of deployment and acquisition of a rail vehicle system composed of one ground mobile robot and two aerial flying robots, considered the actual constraints, minimized the objective function, transformed the path planning problem of the robot system into a multi-constraint optimization problem, and used the PSO algorithm for path planning. [PERSON] et al. [33] proposed a new method of smoothing path planning for mobile robots (PSO-AWDV) algorithm based on a new quart-degree Bezier transition curve and improved particle swarm optimization (PSO) algorithm to solve the problem of path smoothing of mobile robots. [PERSON] et al. [34] proposed an improved FOA algorithm (ORPFOA) to solve the problem of multi-UAV path planning in the three-dimensional complex environment of online transformation tasks, which realized a fast and stable solution by using reference points and distance cost matrices. [PERSON] et al. [35] proposed an improved genetic algorithm for the path-planning problem of multiple automated guided vehicles (multi-AGV). A three-exchange cross heuristic is used to produce more optimized descendants for more information, and a two-path constraint that minimizes the total path distance of all AGVs and minimizes the single-path distance of each AGV is imposed. [PERSON] et al. [36] established a multi-UAV path planning model (MUPPEC) with energy constraints for UAVs to perform certain monitoring tasks within a specified time and proposed a hybrid discrete intelligent algorithm based on gray wolf optimizer (HDGWO) to solve the MUPEC problem. [PERSON] et al. [37] proposed an adaptive multi-UAV path planning method AP-GWO to improve the gray wolf algorithm for slow convergence and unsmooth flight path planning, which improved the convergence speed of the algorithm and the efficiency of UAV task completion. [PERSON] et al. [38] proposed a hybrid IPSO-EOPs algorithm in the multi-robot path planning problem to enhance the exploration and development ability of robots to achieve better convergence, and simulation experiments show that the IPSO-EOPs algorithm shows better results for different numbers of robots. As an important branch of machine learning in artificial intelligence algorithms, reinforcement learning is widely used in science, engineering, art, and other fields. [PERSON] et al. [39] proposed a FANET multi-objective optimization routing protocol based on Q-learning, aiming at the problem that the existing routing protocols rarely meet the low latency and low energy consumption requirements of FANET, and the Q-learning parameters can be adaptively adjusted in the proposed protocol to adapt to the high dynamics of FANET. [PERSON] et al. [40] used Q-learning algorithms to solve the problem of path planning on various terrains by self-reconstructing modular robots. In order to overcome the problem of low learning efficiency of Q-learning, [PERSON] et al. [41] proposed the IQL algorithm, which introduced the characteristics of distance measurement, improved Q function, and virtual motion target into the IQL algorithm. [PERSON] et al. [42] proposed a dynamic obstacle avoidance path planning method for manipulators based on the deep reinforcement learning algorithm soft actor-critic (SAC) to solve the problem of difficult path planning of manipulators in a dynamic environment. [PERSON] et al. [43] proposed a DDQN-based path planning algorithm for the global static path planning problem of amphibious USV, which combines electronic charts and elevation maps to construct an environmental model and integrates an action mask method to deal with the invalid actions generated by amphibious USVs on the basis of prior knowledge. [PERSON] et al. [44] combined deep Q-learning and CNN algorithms to generate a new learning algorithm for the problem that traditional path planning algorithms cannot actively respond to variable conditions in multi-robot systems. [PERSON] et al.