A New Method of Motion Planning for Mobile Robots Based on Improved JPS and Polynomial Trajectory Planning

With the rapid development of technology, the mobile robots get a wide range of applications. Path planning algorithms play an extremely crucial role when mobile robots autonomously navigate in complex environments. The current path planning algorithms for complex grid maps have problems, such as low efficiency and unsmooth paths. In order to solve those problems, this paper proposes the fusion of multi-segment polynomials for jump point search algorithm, which realizes the construction of collision-free and path-smooth trajectories on complex raster maps. First of all, the heuristic function of the jump point search has been improved by adding weight factors, distance factors, and directional factors. Secondly, a line-of-sight method has employed to prune the path point set, which optimizes the drawback of excessive node exploration of traditional jump point search algorithm. Lastly, the multi-segment polynomial method is used to smooth the trajectory, which aims to improve the drawbacks of traditional path planning algorithms where the trajectories are not smooth. What's more, this study adopts a closed-form solution for the multi-segment polynomial to enhance the overall convergence speed of the algorithm. The experimental results presented in this paper demonstrate that our algorithm outperforms the Theta and A* algorithms in terms of memory consumption by 97% and 15.7%. In comparison to JPS and A*, improved JPS demonstrates improvements of 73% and 97%, in terms of node count. Furthermore, in comparison to JPS and A*, improved JPS demonstrates increases of 86% and 97%, in terms of total path angular change. In terms of time efficiency, our approach shows an enhancement of 8%, 12%, 15.9%, and 25% when compared to JPS, Theta, A*, and BOA algorithms. Additionally, our method indicates an increment in total path length by 7.3%, 33.5%, 36.3%, and 33% compared to JPS, Theta, A*, and BOA.