Research on obstacle avoidance method of micro robot based on locust vision
In order to improve the stability and reliability of autonomous obstacle avoidance of robots,a bionic visual obstacle avoidance system suitable for embedded micro robots is constructed by optimizing the lobula giant movement detector(LGMD)neural network of locust with collision warning ability in the grasshopper neural system.Aiming at the poor collision perception performance of LGMD network in dark environment,traditional image processing algorithm is combined with the bionic network.By fusing the excitation of Laplace sharpening and Gaussian blur to enhance the extended edge of the collision object,a collision detection neural network based on image enhancement(LGMD-LS)is proposed.The video simulation analysis of the model using MATLAB software shows that compared with LGMD model,the improved algorithm can effectively identify approaching obstacles in dark environment and has better robustness.Physical verification is carried out on the self-made micro robot.The results show that the robot can effectively avoid obstacles in dark scenes,which verifies the reliability of the algorithm.It provides a reference basis for robot collision detection in practical scenes.
mobile robotlocust visual neural networkdynamic obstacle avoidancecollision detection
万方数据
维普
