# Research on an Optimal Path Planning Method Based on A* Algorithm for Multi-View Recognition
## 1 Introduction
Currently, the path planning algorithms proposed by domestic and foreign scholars can be divided into sample-based planning algorithms and search-based combined planning algorithms. Sample-based planning algorithms capture the connectivity properties in an infinite number of free spaces approximately according to the conditions to be met using a countable set of sampling points or sampling sequences. Some classical methods include the Probabilistic Road Map (PRM), Rapid-Exploring Random Tree (RRT), Artificial Potential Field (APF), etc. PRM [31] is a typical global planning algorithm, and map information (including all barrier information) is fully known. The PRM algorithm can be directly used in the path planning of different tasks under the constant obstacle environment with simple operation and probability completeness. It is suitable for static offline planning. However, the PRM algorithm requires a lot of time and memory. The more degrees of freedom the manipulator has, the more complex random sampling and collision detection are. RRT, as a one-way query global path planning algorithm uses a tree structure for node-to-target planning [32; 33]. The algorithm satisfies probability completeness and has lower planning efficiency for high-dimensional sampling space. The methods based on APF [34; 35] calculate the force of gravity and repulsion to drive the robot to the target point. They are suitable for low-dimensional and high-dimensional space planning with simple models. Meanwhile, deficiencies also exist, such as to the danger of falling into local optimum and insensitivity to dynamic obstacle avoidance. The search-based combinatorial programming algorithm searches a path after constructing a topological graph in free space using a graph search algorithm. The commonly used algorithms include Dijkstra [36] and A* algorithms [37]. The Dijkstra algorithm generally solves the shortest path problem in directed graph. It works by using greedy algorithms. Each iteration, it traverses a vertex nearest to the starting point, and each iteration traverses adjacent vertices from vertices that have not been visited, until it extends to the target point. The A* algorithm combines information from the Dijkstra algorithm with the Breadth-First Search (BFS) algorithm for path searching. The A* algorithm is also often used to solve the shortest path in a static road network, which is one of the commonly used heuristic algorithms. In recent years, with the rise of in-depth learning, some scholars have applied machine learning methods in the path planning of mechanical arms, and have accomplished more achievements. [PERSON] et al. [38] proposed motion planning networks (MPNets) based on deep neural networks, which achieve end-to-end planning. However, the data depended on the RRT algorithm and had some limitations. [PERSON] et al. [39] transformed the shortest path problem of the manipulator path planning into a linear programming problem, which was solved using a zeroing neural network (ZNN). A zero-return neurodynamic method was constructed, and a ZNN model was established for the shortest path problem of the manipulator. Then, the stability of the ZNN model was proved by the Lyapunov method. Finally, the ZNN model was applied in the path planning of the manipulator to generate the optimal planning path. [PERSON] et al. [40] combined reinforcement learning with an intelligent optimization algorithm, and proposed a deep deterministic policy gradient-adaptive ant colony optimization algorithm (DDPG-AACO) for complex parts of welding manipulators. Deep deterministic policy gradient (DDPG) training was used to learn local path planning strategies to control the safe movement of a welding manipulator between two weld endpoints. Then, global path planning, with the shortest path length and traversing all seams under the constraint of the welding direction, was achieved using an adaptive ant colony optimization algorithm (AACO) that manually alters the pheromone value. The simulation results showed the effectiveness of this method.