基于AGD-RRT算法的机械臂路径规划

针对RRT（rapidly-exploring random tree）算法在进行机械臂路径规划过程中存在的拓展导向性差、冗余节点多、路径质量差等问题，提出了一种基于AGD-RRT（adaptive goal-directed RRT）的算法。首先，该算法构建了一种动态的目标偏向概率函数，实时调整对目标点进行采样的概率以达到自适应目标导向的效果，减少了无用节点的生成，提高了收敛速度。其次，采用贪婪收敛策略，防止了随机树在目标周围时的盲目扩张。搜索结束后，采用节点剔除法剔除路径中的冗余节点，并用B样条曲线对轨迹进行平滑处理，提高了路径质量。然后在二维、三维环境中进行了对比仿真实验，验证了该算法的可行性与优越性。最后进行了样机实验，验证了所提算法在机械臂关节空间进行路径规划的可行性。

Path Planning of Manipulators Based on AGD-RRT Algorithm

In view of such flaws as poor expandability, multiple redundant nodes, and poor path quality of RRT（rapidly-exploring random tree） in the path planning process of manipulators, an algorithm based on AGD-RRT (adaptive goal-directed RRT) has been proposed to address these above-mentioned problems. Firstly, the algorithm constructs a dynamic goal-directed probability function that adjusts the sampling probability of target points in real-time to achieve adaptive target orientation, thus reducing the generation of useless nodes and improving convergence speed. Secondly, a greedy convergence strategy is adopted for the prevention of a blind expansion of the random tree around the target. With the search completed, the node removal method is used to remove redundant nodes in the path, with the trajectory removed by B-spline curves for an improvement of the quality of the path. Then, comparative simulation experiments are to be conducted in 2D and 3D environments to verify the feasibility and superiority of the algorithm. Finally, a prototype experiment is conducted to verify the feasibility of the proposed algorithm for path planning in the joint space of the manipulator.