殷雄, 陈炎, 郭文豪, 杨子辰, 陈汉歆, 廖安, 姚道金(通讯作者). 基于改进Informed-RRT star的机械臂柔性抓取[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2024.04.07.001
引用本文: 殷雄, 陈炎, 郭文豪, 杨子辰, 陈汉歆, 廖安, 姚道金(通讯作者). 基于改进Informed-RRT star的机械臂柔性抓取[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2024.04.07.001
Flexible grasping of robot arm based on improved Informed-RRT star[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2024.04.07.001
Citation: Flexible grasping of robot arm based on improved Informed-RRT star[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2024.04.07.001

基于改进Informed-RRT star的机械臂柔性抓取

Flexible grasping of robot arm based on improved Informed-RRT star

  • 摘要: 为实现机械臂对目标物体的抓取及对障碍物的躲避,基于传统的Informed -RRT*,提出一种GI- RRT*(Grasping Informed-RRT*)算法。首先,选择三指气动柔性夹爪,设计柔性抓取模块,构建机械臂的自主抓取系统;其次,利用生成残差卷积神经网络(GR-ConvNet)模型预测,输入深度相机采集的彩色图像和深度图像,输出视场中物体的适当映射抓取位姿;最后,预先设定最大迭代次数和自适应函数,利用二次B样条曲线对路径进行约束,生成无碰撞最优机械臂运动轨迹。为验证机器人手臂的抓取效果,分别进行仿真实验和在协作机械臂FR3上进行抓取实验。结果表明,与传统的Informed-RRT*算法相比,改进的机械臂运动规划算法可将轨迹长度缩短10.11%,轨迹生成时间缩短62.68%。机械臂能独立地避开障碍物和抓取目标物体,满足机械臂自主抓取的要求。

     

    Abstract: In order to grasp objects and avoid obstacles, a Grasping Informed-RRT* algorithm is proposed based on the traditional Informed-RRT* algorithm. Firstly, the three-finger pneumatic flexible gripper is selected, the flexible grasping module is designed, and the autonomous grasping system of the manipulator is constructed. Secondly, the generated residual convolutional neural network (GR-ConvNet) model is used to predict that the color image and depth image acquired by the depth camera are input and the appropriate mapping capturing pose of the object in the field of view is output. Finally, the maximum number of iterations and the adaptive function are set in advance, and the path is constrained by quadratic B-spline curve to generate the collished-free optimal trajectory of the manipulator. In order to verify the grasping effect of the robot arm, the simulation experiment and the grasping experiment on the cooperative robot arm FR3 were carried out respectively. The results show that compared with the traditional Informed-RRT* algorithm, the improved algorithm can shorten the trajectory length by 10.11% and the trajectory generation time by 62.68%. The robot arm can avoid obstacles and grasp the target object independently, which meets the requirement of autonomous grasp of the robot arm.

     

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