Constrained Sampling-Based Planning for Grasping and Manipulation
Constrained Sampling-Based Planning for Grasping and Manipulation
复制标题
基于约束采样的抓取和操作规划
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Daniel D. Lee
中科院分区：
文献类型：
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作者：
Jinwook Huh;Bhoram Lee;Daniel D. Lee
提出了一种新的约束，采样为基础的运动规划方法的抓取和运输任务的冗余机器人机械手。我们利用规划的利润率抓与约束，允许最佳的把握配置和方法的方向自动确定。对于具有多个自由度的机械手，当存在许多冗余解时，我们的方法有效地选择了最优抓取位姿。该方法还引入了一个参数化的中间姿态，优化确定的方法方向，增加传感器的不确定性和执行错误下的鲁棒性。我们的方法还考虑使用快速探索随机树（RRT）算法，通过适当的成本惩罚，结合软约束，将抓取的对象运输到所需的目标位置。我们证明了我们的算法在一些模拟和实验应用的有效性和效率。我们的实验结果表明，与以前研究的方法相比，计算效率有显着提高。
This paper presents a novel constrained, sampling-based motion planning method for grasp and transport tasks with a redundant robotic manipulator. We utilize a planning margin for grasping with constraints that allow the best grasp configuration and approach direction to be determined automatically. For manipulators with many degrees of freedom, our method efficiently chooses the optimal grasp pose when there are many redundant solutions. The method also introduces a parameterized intermediate pose that is optimized to determine the approach direction, increasing robustness under sensor uncertainty and execution errors. Our method also considers transporting the grasped object to the desired target position using a Rapidly-exploring Random Tree (RRT) algorithm that incorporates soft constraints via appropriate cost penalties. We demonstrate the effectiveness and efficiency of our algorithms on a number of simulated and experimental applications. Our experimental results show a marked improvement in computational efficiency in comparison to previously studied approaches.