# Research on an Optimal Path Planning Method Based on A* Algorithm for Multi-View Recognition
## 1 Introduction  
With the rapid development of artificial intelligence, it is an inevitable trend to realize intelligence for robots that integrate machine vision technology. Different images observed from different perspectives provide information on different features. Information in a certain direction from a fixed perspective cannot satisfy recognition requirements. Dynamic observation from high-quality multi-view angles can obtain more comprehensive features of the target, which can improve the recognition accuracy greatly. Series linkage robots are widely used in the industrial field with simple structures and flexible operations. It depends on the path planning of the manipulator whether the mechanical arm can complete tasks efficiently and accurately in the workspace. Therefore, in order to improve the target recognition accuracy of the robot in different environments, it is of great significance to plan the motion of the manipulator to obtain the optimal multi-view observation path of the target. Selection of perspectives, motion of cameras and path planning of mechanical arms have been researched separately in general. For the selection of perspectives, the traditional selection methods can be divided into two types, based on geometric features and semantic features according to the different measurement criteria. Viewpoint metrics based on geometric features include profile curvature [1], depth map [2], region of interest [3], curvature variation of target projection on viewpoint-related imaging plane [4], etc. Local geometricfeatures reflect details which cannot represent the main content of the target. Scholars have proposed some methods based on information theory, such as viewpoint interaction information [5], relative entropy [6], viewpoint visibility KL distance [7], and surrogate information [8]. These methods can be used to further measure global features based on geometrical property measurement. For features such as texture and functionality, which are called semantic features, the proposed viewpoint metrics methods include discernibility region [9], upright direction [10], implication segmentation [11], Schelling Points [12], etc. The viewpoint metrics method based on geometric feature detection is simple and fast, but the measurement quality is far from expected. The method based on semantic feature detection can generally obtain good results, but its operation is complex and inefficient. With the development of artificial intelligence, deep learning is also gradually applied to the study of viewpoint selection. With the introduction of long short-term memory networks (LSTM) [13], multi-loop-view convolutional neural networks (MLVCNN) [14], the hierarchical multi-view context modeling method (HMVCM) [15] and others combined with compact 3D shape descriptors, [PERSON] et al. [16] presented a view-based convolution neural network (view-GCN), and aggregated the multi-view features as a global shape descriptor. [PERSON] et al. [17] proposed a multi-view saliency guided depth neural network, which consisted of a model projection-rendering module, visual context learning module and multi-view learning module. In the visual context learning module, convolutional neural network was used to extract the visual features of a single view, and then the salient LSTM was used to select the representative view adaptively. Multi-view learning modules generated compiling three-dimensional object descriptors using the designed LSTM classification. The method utilized a multi-view context to select representative views and measure similarity. [PERSON] et al. [18] proposed a target detection model based on a convolution neural network, which was combined with a three-dimensional CAD model to select viewpoints. The above viewpoints were preset, all views were entered into the network, and the best performance could be achieved by using all views. In order to reduce calculation, RotationNet [19] only needed a few images.