Research on collision avoidance system for four-wheeled robot based on multi-sensor fusion

Abstract This paper investigates a collision avoidance system for four-wheeled robots based on multi-sensor fusion, aiming to improve the robustness of the robot in complex and variable environments. To address the problem of system performance degradation due to a single sensor’s failure, this paper adopts multi-sensor data fusion technology, which integrates multiple data sources such as visual sensors and LiDAR, to perceive the environment comprehensively. At the same time, the data processing and decision-making algorithms are optimized in conjunction with robust design techniques to ensure that the system can continue to operate stably in the face of uncertainty and unexpected situations. In this study, PreScan is used to conduct simulation and modeling experiments, and the results show that the system effectively enhances the fault tolerance and robustness of the four-wheeled robot, thus providing substantial support for the safe implementation of robotics.