Rapidly Exploring Random Trees (RRT) is one of the most widely used algorithms for motion planning in the field of robotics. To reduce the exploration time, RRT-Connect was introduced where two trees are simultaneously formed and eventually connected. Probabilistic RRT used the concept of position probability map to introduce goal biasing for faster convergence. In this paper, we propose a modified method to combine the pRRT and RRT-Connect techniques and obtain a feasible trajectory around the obstacles quickly. Instead of forming a single tree from the start point to the destination point, intermediate goal points are selected around the obstacles. Multiple trees are formed to connect the start, destination, and intermediate goal points. These partial trees are eventually connected to form an overall safe path around the obstacles. The obtained path is tracked using an MPC + Stanley controller which results in a trajectory with control commands at each time step. The trajectories generated by the proposed methods are more optimal and in accordance with human intuition. The algorithm is compared with the standard RRT and pRRT for studying its relative performance.

翻译：快速探索随机树（RRT）是机器人运动规划领域应用最广泛的算法之一。为缩短探索时间，研究者提出了RRT-Connect算法，该算法通过同时构建两棵随机树并最终实现连接。概率性RRT（pRRT）利用位置概率图的概念引入目标导向机制以加速收敛。本文提出了一种改进方法，融合pRRT与RRT-Connect技术，在障碍物周围快速生成可行轨迹。不同于从起始点到目标点构建单一随机树，本方法在障碍物周边选取中间目标点，分别构建连接起始点、目标点与中间目标点的多棵局部随机树。这些局部树最终连接形成绕过障碍物的全局安全路径。通过MPC与Stanley控制器联合跟踪该路径，可获得包含各时间步控制指令的轨迹。实验表明，本文方法生成的轨迹更优且符合人类直觉。算法性能与标准RRT及pRRT进行了对比验证。