A Robot Arm Motion Planning Algorithm Guided by Cost Function
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摘要: 本文提出一种基于连杆运动方程的人工势场，引导基于转换的快速随机扩展树（T-RRT）改进算法采样，在高维度空间搜索低成本路径的同时解决机械臂运动规划中T-RRT算法收敛速度慢的问题。简化机械臂模型以提高碰撞检测的效率，并与运动学分析结合调制连杆运动方程，从而确定各质点轨迹长度、叠加以建立机械臂人工势场，作为代价函数判断状态节点的成本，引导其不断向目标位置逼近，同时为了进一步提高算法的扩张速度，引入剪枝函数对细化节点进行限制。在不同的障碍地图中进行MATLAB仿真实验，该算法与RRT、T-RRT算法相比，路径长度最短、节点采样效率最高、节点平均采样时间最优，运行时间分别缩短了约3/4及2/3。所提算法在提高路径质量的同时有效提高搜索效率，能适应环境的变化。Abstract: The artificial potential field (APF) based on link motion equation is proposed to guide the sampling of improved Transition-based RRT (T-RRT), quickening the convergence speed of the T-RRT algorithm in the motion planning of a robot arm when it is searching the low-cost path in a high-dimensional space. The manipulator model is simplified to improve the efficiency of collision detection and to construct the link motion equation through kinematics analysis. Then the sum of trajectory length of every particle is calculated to build the APF. As a cost function to judge state nodes, the APF guides the state nodes that approach the target position. Besides, in order to raise the expansion speed of the algorithm, the pruning function is introduced to restrict refinement nodes. Various obstacle maps are simulated with MATLAB. Compared with RRT and T-RRT algorithms, our algorithm has the shortest path length, the highest node sampling efficiency, the shortest node average sampling time, with the running time being shortened by about 3/4 and 2/3 respectively. The proposed algorithm can effectively enhance search efficiency, improve the path quality and adapt to environmental change.
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Key words:
- robot arm /
- improved T-RRT /
- link motion equation /
- APF /
- kinematics analysis /
- MATLAB
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表 1 连杆参数
i 1 2 3 4 5 6 ai/cm 0 9 9 9 0 0 di/cm 0 0 0 0 0 7 αi/(°) 90 0 0 0 90 0 θi/(°) 0 0 0 0 0 0 
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