Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province, School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China.
Sensors (Basel). 2023 Jan 16;23(2):1041. doi: 10.3390/s23021041.
In order to address the shortcomings of the traditional bidirectional RRT* algorithm, such as its high degree of randomness, low search efficiency, and the many inflection points in the planned path, we institute improvements in the following directions. Firstly, to address the problem of the high degree of randomness in the process of random tree expansion, the expansion direction of the random tree growing at the starting point is constrained by the improved artificial potential field method; thus, the random tree grows towards the target point. Secondly, the random tree sampling point grown at the target point is biased to the random number sampling point grown at the starting point. Finally, the path planned by the improved bidirectional RRT* algorithm is optimized by extracting key points. Simulation experiments show that compared with the traditional A*, the traditional RRT, and the traditional bidirectional RRT*, the improved bidirectional RRT* algorithm has a shorter path length, higher path-planning efficiency, and fewer inflection points. The optimized path is segmented using the dynamic window method according to the key points. The path planned by the fusion algorithm in a complex environment is smoother and allows for excellent avoidance of temporary obstacles.
为了解决传统双向 RRT算法随机性高、搜索效率低以及规划路径拐点多等缺点，我们从以下几个方面进行了改进。首先，针对随机树生长过程中随机性高的问题，通过改进的人工势场法约束起始点处随机树的扩展方向，使随机树朝着目标点生长。其次，目标点生长的随机树采样点向起始点生长的随机数采样点倾斜。最后，通过提取关键点对改进的双向 RRT算法规划的路径进行优化。仿真实验表明，与传统 A*、传统 RRT 和传统双向 RRT相比，改进的双向 RRT算法具有路径长度更短、路径规划效率更高、拐点更少的优点。使用关键点对优化后的路径进行分段，使用动态窗口法对融合算法在复杂环境中规划的路径进行分段，使规划的路径更加平滑，可以很好地避开临时障碍物。
