基于RSS_GN RRT算法的狭长空间路径规划

针对RRT算法在狭长空间中存在的收敛速度慢及规划路径不平滑的问题，提出了一种RSS＿GN RRT算法。为了提升算法的收敛速度，提出了引导节点导向策略与分区域采样策略，极大地减少了算法对无效区域的搜索；其次，算法引入了采样角度约束策略来提高规划路径的质量，并采用父节点拓展选择的方法有效解决了由角度约束引起的迭代次数增加的问题。此外，算法可根据感知信息进行地图地动态重构并规划避障路径，提高了算法在低速动态环境中的适应性。仿真结果显示，在狭长通道环境中，RSS＿GN RRT算法在规划路径的耗时上比RRT、Goal＿bias RRT、角度约束下的RRT、Informed RRT*及DR-RRT算法分别减少了77.3%,51.9%,84.7%,98.8%和60.3%。在迭代次数上，相比于上述算法，分别减少了95.9%,92%,98.3%,98.3%和89.5%。路径的平均曲率也分别降低了94.1%,93.2%,88.7%,91%和92.9%。仿真结果证明了RSS＿GN RRT算法在提升规划速度和改善路径质量方面具有显著优势。同时，本文采用了阿克曼模型的小车实测了算法的局部避障能力。经测试，小车可...

Narrow and long space path planning based on RSS_GN RRT algorithm

Aiming at the problems of RRT algorithm in narrow and long space, including slow convergence speed and rough planned path, the RSS_GN RRT algorithm was proposed. To enhance the algorithm’s convergence speed, a guide node-oriented strategy and a regional sampling strategy was proposed, greatly reducing the search for invalid regions. Next, the sampling angle constraint strategy was introduced to improve the planned path quality, and adopted the method of parent node expansion selection to effectively solve the problem of increased iteration times caused by angle constraint. Furthermore, the algorithm can dynamically reconstruct the map and plan obstacle avoidance path based on the perception information, enhancing its adaptability in the low-speed dynamic environment. The simulation results show that in a narrow and long channel environment, the RSS_GN RRT algorithm reduces path planning time by 77.3%, 51.9%, 84.7%, 98.8%, and 60.3% when compared to the RRT, Goal_bias RRT, RRT under angle constraint, Informed RRT*, and DR-RRT algorithms, respectively. It decreases the number of iterations by 95.9%, 92%, 98.3%, 98.3%, and 89.5% relative to above algorithms. The average curvature of the path is also reduced by 94.1%, 93.2%, 88.7%, 91%, and 92.9%, respectively. The simulation results prove that RSS_GN RRT algorithm’s significant advantages in enhancing planning speed and optimizing path quality. Simultaneously, this paper uses the Ackerman model car to actually measure the local obstacle avoidance ability of the algorithm. After testing, the car can reasonably avoid obstacles that appear during driving.