工程科学学报2025，Vol.47Issue(1)：113-120,8.DOI:10.13374/j.issn2095-9389.2024.04.07.001
基于改进Informed-RRT*的机械臂抓取运动规划
Flexible grasping of robot arm based on improved Informed-RRT star
摘要
Abstract
With advancements in science and technology,collaborative and industrial robotic arms are increasingly gaining popularity.Enhancing the intelligence and autonomy of robot arms,particularly in autonomous grasping,has become one of the research hotspots in robotics research.To improve the efficiency and success rate of industrial robot arms in grasping target objects and avoiding obstacles,a three-finger pneumatic flexible clamp was selected,and a flexible grasping module was designed.Communication between the upper computer and the single-chip computer via a serial port enables clamping and loosening actions,constructing an autonomous grasping system based on the traditional Informed-RRT*algorithm.An improved info-RRT*algorithm(Grasping informed-RRT*,GI-RRT*)for the GR-ConvNet model is proposed.First,the maximum number of iterations and the adaptive function are pre-set to shorten the generation time of the manipulator's motion trajectory and enhance sampling guidance and quality.Second,direct sampling of elliptical subsets constrains the position of sampling points,improving sampling efficiency.Finally,a greedy algorithm deletes redundant path points,and a cubic B-spline curve smoothly constrains the trajectory of the robot arm,shortening its length and improving flexibility.The generated residual convolutional neural network(GR-ConvNet)model predicts inputs from color and depth images captured by a depth camera,outputting the appropriate mapping grab pose of the object in the field of view.To verify the grasping effect of the robot arm,simulation and grasping experiments were conducted on the cooperative robot arm FR3.Simulation results show that,compared with the traditional Informed-RRT*algorithm,the improved algorithm shortens trajectory length by 10.11%and reduces trajectory generation time by 62.68%.The robot arm independently avoids obstacles and grasps target objects,meeting the requirements for autonomous grasping.Experiments with the cooperative robot arm demonstrate its ability to independently grasp objects independently and successfully avoid obstacles.This further validates the algorithm's effectiveness on a real robot arm,bringing hope for its further development and use.It reduces the difficulty for operators to use the robot arm and accelerates the wide application of domestic robot arms in factories.This paper aims to promote the practical application of robot arms.关键词
柔性夹爪/机械臂/运动规划/信息引导的快速随机树星算法/神经网络Key words
flexible jaw/robotic arm/motion planning/Informed-RRT*algorithm/neural network分类
矿业与冶金引用本文复制引用
殷雄,陈炎,郭文豪,杨子辰,陈汉歆,廖安,姚道金..基于改进Informed-RRT*的机械臂抓取运动规划[J].工程科学学报,2025,47(1):113-120,8.基金项目
国家自然科学基金资助项目(52365003,52165069,52367015) （52365003,52165069,52367015）
江西省自然科学基金资助项目(20232BAB214045,20224BAB214051,20224BAB204051,20232BAB214064) （20232BAB214045,20224BAB214051,20224BAB204051,20232BAB214064）
江西省重点学科学术技术带头人培养计划资助项目(20232BCJ23027) （20232BCJ23027）
江西省重点研发计划资助项目(20212BBE51010) （20212BBE51010）
江西省研究生创新专项资金资助项目(YC2023-S468,YC2024-S428) （YC2023-S468,YC2024-S428）
