Analysis and Prospective Outlook of Robot Path Planning Methods

Robot path planning is one of the key issues in the field of robotics. Its goal is to guide robots to find a safe and efficient optimal path from the starting point to the destination in complex environments. With the rapid development of artificial intelligence, sensor technology, and computing power, path planning methods have evolved from traditional geometric algorithms to intelligent decision-making systems based on deep learning and reinforcement learning.This paper systematically reviews the development history of robot path planning technology, analyzes the working principles of classic algorithms and cutting-edge methods, and compares the advantages and disadvantages of different technologies. Early studies such as the A* algorithm and the artificial potential field method solved the problem of generating global paths in static environments. However, as application scenarios became more complex, requirements for dynamic obstacle avoidance and real-time performance prompted researchers to turn to real-time algorithms, such as evolutionary algorithms and neural networks. In recent years, the integration of deep learning and reinforcement learning has further promoted the intelligence of path planning, enabling robots to possess the ability to learn through trial and error in unknown environments.However, current technologies still face challenges such as insufficient adaptability to dynamic environments and difficulties in multi-robot collaboration. In the future, the development of frontier technologies such as neural symbolic systems and swarm intelligence optimization is expected to solve these problems.This paper comprehensively analyzes robot path planning technology, aiming to provide theoretical references for researchers in related fields.