Research on robot path planning based on A*-weighted JPS Algorithm

Abstract

Mobile robots are entering daily life quietly. How mobile robots can walk more efficiently in environments with complex obstacles has become a hot topic. For robot path planning, there are currently a variety of research algorithms, which can be divided into global path planning based on prior information and local path planning based on multi-sensor information. Commonly used robot path planning algorithms include artificial potential field method, ant colony algorithm, Genetic algorithm, annealing algorithm, etc., because these algorithms have too much calculation and are difficult to apply to environments with complex obstacles, this paper proposes an improved algorithm based on A* algorithm—weighted JPS algorithm for the above problems. This algorithm takes the expansion of sub-nodes as the optimization focus, reduces the number of random expansions of the current node, reduces the time for the robot to reach the target point, and uses the five-term interpolation method to smooth the planned path to reduce the robot’s travel process the probability of collision due to bypassing obstacles and cornering corners. Experimental results show that compared with the A* algorithm, the weighted JPS algorithm reduces the search time by 20% to 27%, and the length of the path to be explored is also reduced by 2% to 4%.