融合波前边缘检测与快速搜索随机树的自主探索方法

Aiming at the problems that the convergence time of extracting exploration candidate points increases with the expansion of known regions in the exploration process of traditional wavefront edge detection algorithm, and the traditional evaluation function does not consider the robot pose when selecting exploration resulting in inefficient autonomous exploration in unknown environment, an autonomous exploration algorithm combining local and global perspectives is proposed. By limiting the traversal range of the traditional WFD algorithm and running synchronously with the fast search random tree algorithm, the proposed method extracts local and global exploration candidate points respectively, and uses the light simplified firefly algorithm to accept exploration candidate points for clustering. An improved evaluation function is formed by introducing the angular cost into the traditional evaluation function with information gain and path cost to realize the selection of the best exploration target points and guide the robot to complete the efficient exploration of unknown environment. Finally, a simulation and prototype experiment platform based on the robot operating system (ROS) is built for experimental validation, and the results showed that the proposed algorithm reduced the exploration time and exploration path level by 22.19% and 32.13%, respectively, compared with the RRT-BFS algorithm, which improves the efficiency of autonomous exploration.