Obstacle Avoidance Algorithm for Mobile Robots

Abstract

Automatic car obstacle avoidance technology mainly utilizes advanced sensor technology to enhance the car's perception ability of the driving environment, feedback real-time information such as vehicle speed and position obtained by the perception system to the system, and judge and analyze potential safety hazards based on comprehensive information of road conditions and traffic flow. Mobile robots are increasingly used in applications, including in industrial production, agriculture, healthcare, rescue. However, mobile robots often face the challenge of avoiding obstacles while performing their tasks. Therefore, the development of intelligent obstacle avoidance algorithms is crucial to improve the autonomy, safety, and efficiency of mobile robots. In emergency situations, it automatically takes measures such as alarm prompts, braking or turning to assist and control the car to actively avoid obstacles, ensuring vehicle safety. At present, there are also many mature algorithms for local obstacle avoidance, and each algorithm has its own advantages and disadvantages. The current methods mainly include artificial potential field method (APF) and virtual force field method (VFF). This article aims to comprehensively review the latest research progress of obstacle avoidance algorithms for mobile robots. Chapter 2 introduces these methods in four different ways. The remaining part of the paper serves as the conclusion of the research and discusses future obstacle avoidance algorithms.