); [EMAIL_ADDRESS] (W.W.)  
[PERSON]  
1 Guangzhou Institute of Advanced Technology, Chinese Academy of Sciences, Guangzhou 511458, China; [EMAIL_ADDRESS] (W.Y. ); [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (G.Z. ); [EMAIL_ADDRESS] (Z.H. ); [EMAIL_ADDRESS] (W.W.)  
[PERSON]  
1 Guangzhou Institute of Advanced Technology, Chinese Academy of Sciences, Guangzhou 511458, China; [EMAIL_ADDRESS] (W.Y. ); [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (G.Z. ); [EMAIL_ADDRESS] (Z.H. ); [EMAIL_ADDRESS] (W.W.)  
[PERSON]  
1 Guangzhou Institute of Advanced Technology, Chinese Academy of Sciences, Guangzhou 511458, China; [EMAIL_ADDRESS] (W.Y. ); [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (G.Z. ); [EMAIL_ADDRESS]. many feasible algorithms. These algorithms can be divided into global path planning algorithms [7; 8] and local path planning algorithms [9]. The grid algorithm [10], A* algorithm and D* algorithm [11; 12; 13] belong to the former, and the ant colony algorithm [14; 15], artificial potential field method [16] and neural network method [17] belong to the latter. Each algorithm has its corresponding advantages and disadvantages in different scenarios, so it is necessary to apply the most appropriate algorithm in light of the actual conditions. The purpose of this paper is to accomplish the reconnaissance task of amphibious robot fish. From the environmental point of view, it belongs to the completely unknown environment, and the obstacle avoidance problem in this kind of environment belongs to the local path planning problem, which has wider application range and more practical significance than global path planning. The reasons for choosing the artificial potential field method as the local path planning algorithm are listed as follows: (1) it made many achievements as a mature algorithm; (2) the algorithm is relatively simple and the planned path is comparatively smooth, so robots can identify the whole path much more easily; (3) it has a fast calculation speed, high efficiency, and is more suitable for practical application. However, this method has some problems, such as local minimum and unreachable target point [18]. Scholars have also proposed many solutions to solve these problems, such as the improved algorithm combined with an ant colony algorithm or simulated annealing [19], adding virtual obstacles [20] and modifying the force field potential function [21], etc. In this paper, an improved method is proposed to solve the above two problems, and a more appropriate hybrid algorithm for amphibious robots is generated by combining the dynamic window method. The effectiveness of the proposed algorithm is validated through experiments. The rest of the paper is organized as follows. Section 2 introduces the kinetic analysis of the amphibious robot. We listed different dynamic equations and set parameters according to different environments of robotic fish, so as to improve the ability of various movements of robotic fish, so the content of the second chapter is to further examine to the real motion and provide a theoretical basis. Section 3 briefly reviews the existing artificial potential field method and the problem it faces. Section 4 provides a detailed description of the improved algorithm. In short, on a macro level, the purpose of treating the amphibian robot as a virtual mass point is to better show how the improved artificial potential field method can plan the general direction of motion and avoid problems. At the micro level, adding the environmental constraints and robot kinematics parameters (velocity, angular velocity, etc.) is necessary to better fit the actual situation. Section 5 presents and discusses the experimental results for the proposed method. Section 6 discusses the use of the two algorithms. Section 7 concludes the paper.