基于JPS改进的移动机器人路径规划算法

针对跳点搜索（jump point search，JPS）路径规划算法在大尺度复杂场景下存在内存资源消耗较大、路径结果平滑度较低且路径过于靠近障碍物等问题，提出融合安全势场等级函数与优化Floyd算法的改进JPS算法。首先建立了安全等级函数对栅格地图中的栅格状态进行重新赋值构建安全等级地图；然后改进了启发式函数，引入目标与主方向两项偏置函数项结合安全等级函数项，进一步减少对称性搜索带来的时间消耗，改善了所规划路径的安全程度。其次通过添加二次平滑算法流程优化了Floyd算法；最后结合B-spline样条插值法，进一步提高了改进算法所规划路径的平滑程度。仿真实验验证了改进优化算法在内存资源消耗、路径长度、路径平滑程度以及路径安全程度都有显著提升。

Improved path planning algorithm for mobile robot based on JPS

Under a large-scale and complex environment, for the path planning algorithm of JPS, the memory consumption is large, the smoothness of path result is low, and the path result is excessively close to obstacles. Aiming at these problems, this paper proposed an improved JPS algorithm that integrated a level function of safety potential field and an optimized Floyd algorithm. It firstly built the safety level function and reassigned the grid status in the grid map to build a safety level map. Then by introducing two bias functions of goal and cardinal direction, and by combining the safety level function, this paper further reduced the time consumption brought by symmetric search, which improved the safety level of the path planned. Besides, it also optimized Floyd algorithm by adding a flow of second smoothing algorithm. At last, the paper integrated B-spline interpolation, improving the smoothness of path planned by the improved algorithm. The simulation tests verify that there are significant improvements in memory consumption, path length, path smoothness and path safety level for the improved and optimized algorithm.