Research on Reflector Navigation Algorithm and Optimization Smoothing Critical Path Trajectory

For mobile robots working in dense environments, navigation and path planning are crucial to understanding and reproducing human wayfinding thinking patterns. Firstly, this paper analyzed a hybrid SLAM navigation algorithm based on the reflector and laser constructed by the grid map method. Secondly, in the case of Kalman filter localization loss, this paper uses the generalized least squares inverse method to solve the nonlinear equations of the reflector to calibrate the localization information. Thirdly, the report used the RDP(Ramer-Douglas-Peucker algorithm) algorithm to reduce the critical path points of the trajectory, which solves the problem of many path points in the traditional JPS algorithm. At the same time, used the optimal boundary NURBS curve algorithm to improve the robot trajectory quality. Finally, in large-scale simulation and physical environment, the navigation algorithm based on the reflector and the optimized smooth critical path trajectory algorithm was verified, proving the algorithm's effectiveness and accuracy in the complex domain.