# Research on an Optimal Path Planning Method Based on A* Algorithm for Multi-View Recognition
## 1 Introduction
The perspective variable was used as the optimized latent variable in the training process, which could obtain the ability to establish the corresponding viewpoint for the input views. The pose and the category label of the object were estimated. Then, the base axis of the object determined in an unsupervised mean based on the appearance was added to achieve alignment, and the views were enhanced [20]. However, this method mainly finds the corresponding viewpoint for the input view, and still needs to contain a predefined complete multi-view image set. Inspired by the attention mechanism, [PERSON] et al. [21] proposed a view-enhanced recurrent attention model (VERAM), which was a real active viewpoint selection mechanism. It contained a camera module, observation sub-network, cyclic subnet, viewpoint evaluation subnet and classification subnet. The information flow of the reverse gradient was added to balance the perspective prediction network and classification sub-network. The reward and loss functions were designed to improve the performance of view classification and perspective selection. The network can select the shortest view sequence independently and input it into a three-dimensional shape classification network with the active camera viewpoint selection ability. However, the problems appear easily in this mixed system such as too many parameters lead to fit, and difficult to balance the subnets. It is not difficult to find that perspectives selection methods based on deep learning describe the features of the observed objects more comprehensively and the selection results are more representative. For the study of camera motion planning, [PERSON] et al. [22] used a greedy algorithm to obtain the optimal viewpoint set. A method of camera auto-roaming guidance based on a good viewpoint set was proposed, and it was optimized based on semantic distance. [PERSON] et al. [23] planned a virtual camera in an all-round way using heuristic methods to solve the viewpoint coverage problem. [PERSON] et al. [24] fixed the viewpoint height, optimized the camera layout, and converted the three-dimensional problem into a two-dimensional discrete coverage problem. [PERSON] et al. [25] proposed a SmarkMax algorithm, which achieved maximum coverage with a minimum number of viewpoints. [PERSON] et al. [26] proposeda camera relocation method based on multiple output learning. [PERSON] et al. [27] studied the uncertainty in forest regression to achieve accurate camera positioning. [PERSON] et al. [28] proposed a viewpoint selection evaluation method based on the Kullback-Leibler Divergence method. The aesthetic evaluation method was interpreted by the three-point method and diagonal composition method, and the probability density formula with the existence of the target was defined. The optimal viewpoint for searching scenes in the robot photography system was constructed. [PERSON] et al. [29] proposed a framework for answering questions in human-computer interaction by iteratively observing scenes. [PERSON] et al. [30] proposed a spatial search method combining Gaussian process and particle swarm optimization (PSO) algorithm for indoor UAV photography, whose objective was to effectively predict the heuristic optimal viewpoint with short time. With regard to camera motion planning, it can be found that some of the research results do not consider the choice of the optimal perspective, and camera motion planning considering the optimal perspective do not consider factors such as camera motion time and efficiency. For the path planning of the manipulator, the research achievements are mainly focused on the path planning of the manipulator end in Cartesian space and the trajectory optimization of joints in joints space. The purpose of this paper is to optimize the view angle of the camera driven by the end of the manipulator; therefore, only the path planning of the manipulator in Cartesian space is studied in this paper.