Path optimization of a flexible robot with a spatial compressed and a direction-guided exploring method

Abstract

Optimal path planning of a measuring robot is a crucial component in automatic measurement. However, it is hard to obtain a stable optimal path solution when dealing with multiple environments. To overcome this problem, a spatial compressed and direction-guided exploring method is proposed based on Rapidly-exploring Random Tree (RRT). First, to enhance the consistency of the search path, a spatial compaction strategy is incorporated into the direction guidance Rapidly-exploring Random Tree (DG_RRT) method, which optimizes the exploration space for initial path generation. Second, invalid spaces are simplified by model constraints. Third, to obtain more suitable spatial compression models for different environmental features, the generated compressed space is further optimized by adjusting the functional coefficient (FC) and iterations. Path optimization consists of two key steps: linearization for redundant node elimination and curve-fitting for path smoothing. Subsequent discretization is then applied to ensure compatibility with robotic execution constraints. To verify the suggested spatial compressed and direction-guided RRT (SC_DG RRT) method, both numerical simulation and experimental analysis are carried out. The experimental results reveal that the average path length (APL) of SC_DG RRT is 11.4 % lower than that of the DG_RRT and 42.3 % lower than that of Q-learning (QL). The standard deviation (SD) of the path length of SC_DG RRT is 74.3 % lower than that of DG_RRT. It demonstrates that the SC_DG RRT is superior to other methods.