Research on Optimal Trajectory Selection for Full Coverage Search Tasks of Biped Self balancing Mobile Robots
When considering the search task of a bipedal self balancing mobile robot,only the contour features of the object are used as the basis for path calculation,without considering the edge details that may affect the path.This can lead to the problem of less image information collected,and due to the complex search environment and the need to avoid obstacles,the search path is long and inefficient.This paper proposes a full coverage search task trajectory optimization method for bipedal self balancing mobile robots.The robot vision image acquisition results are optimized.The gray-scale transformation function is used to carry out gray-scale transformation on the image.The bilateral filter function is applied to replace the Gaussian filter in the Retinex algorithm.The reflection component of the environment image is obtained,and the R,G,B gray-scale values are weighted and fused to obtain the image that retains the edge and enhances the details when the biped robot fully covers the search task.The image is input into the support vector machine to identify the targets and obstacles in the environment.Based on the obtained environmental information,a simulation mapis established,a path for robot search tasks is constructed,and the optimal path is obtained through ant colony algorithm.The experimental results show that the proposed method can avoid obstacles and reach the target location,with short path distance and high planning efficiency.
NETL
NSTL
万方数据
