A Mobile Grasp Planning Method Based on Iterative Optimization

被引：0
|
作者：
Li W. [1 ]
Zhang Y. [1 ]
Wang P. [1 ]
Xiong R. [1 ]
机构：
[1] Institute of Cyber System and Control, Zhejiang University, Hangzhou
来源：
Jiqiren/Robot | 2019年 / 41卷 / 02期
关键词：
Deep convolutional neural network; Heuristic random path; Inverse kinematics; Iterative optimization; Mobile manipulation;
D O I：
10.13973/j.cnki.robot.180190
中图分类号：
学科分类号：
摘要：
A mobile grasp planning method based on iterative optimization is proposed, which needn't model the target object in advance. The 3D model of the target object is measured and modeled online by the point cloud camera, and the deep convolutional neural network is used to evaluate the success probabilities of the alternative grasp locations generated by the target point cloud. The positions and orientations of the robot base and gripper are optimized iteratively until the robot reaches an optimal configuration when grasping the target object. Then A* algorithm is used to plan a path from the robot current position to the target position. Finally, a heuristic random path approaching algorithm is used to plan the arm motion based on the robot path, so that the robot can walk and grasp at the same time. The deep learning based evaluation algorithm of success probabilities of the alternative grasp locations achieves 83.3% accuracy on Cornell data sets. The proposed motion planning algorithm can get a smoother, shorter and more favorable path for subsequent movements. © 2019, Science Press. All right reserved.
引用
收藏
页码：165 / 174and184
相关论文
共 23 条
  • [1] Beetz M., Stulp F., Esden-Tempski P., Et al., Generality and legibility in mobile manipulation, Autonomous Robots, 28, 1, pp. 21-44, (2010)
  • [2] Petrovskaya A., Ng A.Y., Probabilistic mobile manipulation in dynamic environments, with application to opening doors, International Joint Conference on Artificial Intelligence, pp. 2178-2184, (2007)
  • [3] Stuckler J., Schwarz M., Schadler M., Et al., NimbRo explorer: Semiautonomous exploration and mobile manipulation in rough terrain, Journal of Field Robotics, 33, 4, pp. 411-430, (2016)
  • [4] Mericli T., Veloso M., Akin H.L., Experience guided mobile manipulation planning, AAAIWorkshop-Technical Report, pp. 55-60, (2012)
  • [5] Diankov R., Ratliff N., Ferguson D., Et al., Bispace planning: Concurrent multi-space exploration, Robotics: Science and Systems IV, (2008)
  • [6] Burget F., Bennewitz M., Burgard W., BI<sup>2</sup>RRT<sub>*</sub>: An efficient sampling-based path planning framework for task-constrained mobile manipulation, IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3714-3721, (2016)
  • [7] Weghe M.V., Ferguson D., Srinivasa S.S., Randomized path plan ning for redundant manipulators without inverse kinematics, IEEE-RAS International Conference on Humanoid Robots, pp. 477-482, (2007)
  • [8] Yang T.W., Gao L.N., Ruan Q.Q., Et al., Visual servo technology for coordinated manipulation of a mobile dual-arm manipulator system, Control Theory and Applications, 32, 1, pp. 69-74, (2015)
  • [9] LaValle S.M., Kuffner J.J., Rapidly-exploring random trees: Progress and prospects, 4th International Workshop on the Algorithmic Foundations of Robotics, pp. 293-308, (2001)
  • [10] Kuffner J.J., LaValle S.M., RRT-connect: An efficient approach to single-query path planning, IEEE International Conference on Robotics and Automation, pp. 995-1001, (2000)
123