# Research on an Optimal Path Planning Method Based on A* Algorithm for Multi-View Recognition
## 1 Introduction
[PERSON] et al. [41] proposed a deep learning training method for motion decoupling solution of redundant mechanical arm. [PERSON] et al. [42] set up a multi-objective decision optimization function for route planning based on the idea of reinforcement learning. [PERSON] et al. [43] proposed a hybrid bidirectional rapidly exploring random tree H-BRRT algorithm based on reinforcement learning to solve problems such as slow random generation and convergence generated by the rapidly exploring random tree (RRT) algorithm. Target gravity strategy was introduced to model the random exploration process. Enhanced learning was applied to improved target gravity exploration strategies, and adaptive switch operations were carried out based on cumulative performance. Experiments showed that this strategy improved the search efficiency and shortened the generated path. [PERSON] et al. [44] established a multi-objective optimization formula considering the maneuverability of the manipulator and the maximum torque. The trajectory of the space manipulator was optimized by the machine learning. [PERSON] et al. [45] presented a snake tongue algorithm based on a slope potential field, which was combined with the genetic algorithm (GA) to complete a shorter path search. The reinforcement learning (RL) was further fused to reduce the number of path points, which achieved efficient collision-free path planning for the robot. Sampling-based methods, such as PRM and RRT algorithms, have the characteristic of probability completeness; that is, the solution can be guaranteed, but not necessarily the optimal solution in the presence of solutions as long as the planning or searching took. Compared with the machine learning method, the traditional planning methods can directly plan the route and find the near-optimal path more effectively, but they also face the problems that complex calculation and poor planning timeliness. The A* algorithm has the advantages of rapid response to the environment and direct path search. As a direct search algorithm, it is widely used in path planning problems. In order to ensure the real-time performance in the process of path planning and reducing the speed of planning, this paper established a directed-graph path-planning model. The nodes search method based on directed graph was selected. The A* algorithm has both the features of the fast speed in the depth and obtaining the optimal solution in the width by adding the evaluation function. It has the maximum probability to find the optimal solution and can reduce the redundancy time. Therefore, the A* algorithm was selected for path planning. From the above analysis, it is clear that the current research does not combine the optimal viewpoint selection, camera motion and path planning of mechanical arms. The current path planning studies are generally conducted on the premise of setting the path and obstacle avoidance functions given the starting point and end point, which have no awareness of autonomous end point selection. In order to make the most widely used linkage robots in the industrial field complete the accurate identification of the operated target and have the awareness of autonomous viewpoint selection with the help of machine vision technology, this paper integrates the camera and the end of the robot arm together, and combines the optimal viewpoint selection, camera motion and path planning of the mechanical arm to conduct the research on viewpoint-seeking path planning technology. Specifically, the main contributions of this paper are as follows:1. A multi-view feature extraction model based on the BM2 DKPCANet is constructed and a multi-view classifier combing with Softmax is built. The output category posterior probability is used to characterize the viewpoint performance. 2. The recognition performance and interval distance are abstracted as directed graph models and the multi-objective optimization problem is transformed into a single optimization problem by fusing two adjacency matrices.