Volume 41, Number 3, June 2023
|Page(s)||500 - 509|
|Published online||01 August 2023|
Research on obstacle avoidance motion planning method of manipulator in complex multi scene
School of Electronics and Information, Xi'an Polytechnic University, Xi'an 710048, China
2 Xi'an Polytechnic University Branch of Shaanxi Artificial Intelligence Joint Laboratory, Xi'an 710048, China
In order to improve the efficiency and success rate of obstacle avoidance motion planning of industrial manipulator in complex multi scenes, a collision detection model between manipulator and obstacles based on cylinder and sphere bounding box is established, and an improved RRT* algorithm based on heuristic probability fusion artificial potential field method(P-artificial potential field RRT*, PAPF-RRT*) is proposed. The probability target bias and random sampling point optimization strategy are introduced into the sampling, and the location optimization constraints are applied to the sampling points to enhance the sampling guidance and quality. In order to change the expansion direction of the traditional new node and the local optimization problem in special environment, the target gravity, obstacle repulsion and adaptive step size of the artificial potential field method are combined, so that the algorithm can guide the expansion direction and step size of the new node in real time within the resultant force range generated by APF, reducing excessive exploration and the expansion of the collision region. The Cubic B-spline is used to interpolate and optimize the planned path to reduce the complexity of the path and improve the flexibility of the path. The simulation results in two-dimensional and three-dimensional multi scenes show that the present algorithm reduces the average path search time by 31.22% and shortens the path length by 17.32% comparing with the traditional RRT* algorithm. The visual simulation results show that the present algorithm can make the manipulator successfully avoid obstacles and run to the target point quickly and smoothly.
为提高工业机械臂在狭窄通道、多障碍物等复杂多场景下避障运动规划的成功率和效率, 建立了基于圆柱体和球体包围盒机械臂与障碍物之间的碰撞检测模型, 并提出了一种基于启发式概率融合人工势场法的改进型RRT*算法(P-artificial potential field-RRT*, PAPF-RRT*)。采样上引入概率目标偏向与随机采样点优选策略, 对采样点进行位置优选约束, 增强采样导向性和质量; 为改变传统新节点扩展方向和特殊环境下局部最优问题, 融合人工势场法的目标引力与障碍物斥力和自适应步长, 使算法在APF产生的合力范围下实时引导新节点扩展方向和步长大小, 降低过度的探索和碰撞区域扩展; 对冗余节点进行删除, 并采用三次B样条插值优化, 提高机械臂轨迹的柔顺性。仿真结果表明, 所提算法较传统RRT*算法在平均路径搜索时间上降低了56.75%, 路径长度缩短了17.74%。导入机械臂模型后可视化仿真结果证明, 所提算法可使机械臂成功避障且快速平稳运行到目标点。
Key words: manipulator motion planning / asymptotically optimal rapidly exploring random tree / rapidly exploring random tree algorithms / artificial potential field / cubic B-spline / adaptive step-size
关键字 : 机械臂运动规划 / 渐进最优快速拓展随机树算法 / 启发式概率 / 人工势场法 / 自适应步长 / 三次B样条
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