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基于冗余度的机器人操作臂任务约束运动规划

Redundancy-Based Motion Planning with Task Constraints for Robot Manipulators.

作者信息

Zhang Yi, Wang Hongguang

机构信息

State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Chuangxin Road 135, Shenyang 110016, China.

University of Chinese Academy of Sciences, Yuquan Road 19, Beijing 100049, China.

出版信息

Sensors (Basel). 2025 Mar 19;25(6):1900. doi: 10.3390/s25061900.

DOI:10.3390/s25061900
PMID:40293075
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11945819/
Abstract

Finding realistic motions for redundant manipulators is essential for complex jobs such as home care and industrial assembly. Motion planning is complex when a task requires standing upright or moving through restricted spaces. This work provides an effective motion-planning strategy for 7-DOF manipulators that improves connections via redundancy. The analytic Cartesian-space-to-joint-space kinematic mapping models for 7-DOF redundant manipulators with diverse configurations are constructed first, and the feasible nodes are determined by sampling the Cartesian space without barriers to satisfy the task requirements. Each Cartesian-space sampling node can provide numerous feasible joint-space nodes because of the redundancy of the robot manipulators. To remove additional valid nodes from a singular position, joint configurations with the same end-effector position orientation are modified iteratively. Finally, we find the nearest nodes in the joint-space constraint manifold and build collision-free smooth pathways. The task constraint levels were varied for a 7-DOF manipulator in simulations and experiments. The proposed planner finds more viable nodes at the same end-position attitude than one-to-one projection. It does not require numerical iterations and achieves high planning efficiency and a high motion-planning success rate.

摘要

为冗余机械手找到逼真的运动对于诸如家庭护理和工业装配等复杂工作至关重要。当任务需要直立或在受限空间中移动时,运动规划会变得复杂。这项工作为7自由度机械手提供了一种有效的运动规划策略,该策略通过冗余性改善连接。首先构建了具有不同配置的7自由度冗余机械手的解析笛卡尔空间到关节空间的运动学映射模型,并通过对无障碍笛卡尔空间进行采样来确定可行节点,以满足任务要求。由于机器人机械手的冗余性,每个笛卡尔空间采样节点都可以提供大量可行的关节空间节点。为了从奇异位置去除额外的有效节点,对具有相同末端执行器位置方向的关节配置进行迭代修改。最后,我们在关节空间约束流形中找到最近的节点并构建无碰撞的平滑路径。在模拟和实验中,针对7自由度机械手改变了任务约束级别。与一对一投影相比,所提出的规划器在相同的末端位置姿态下找到了更多可行节点。它不需要数值迭代,实现了高规划效率和高运动规划成功率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7b5/11945819/bd265f780212/sensors-25-01900-g009a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7b5/11945819/b45373ff7718/sensors-25-01900-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7b5/11945819/813fea80c9aa/sensors-25-01900-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7b5/11945819/dc58073d42e1/sensors-25-01900-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7b5/11945819/b973bddfbeae/sensors-25-01900-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7b5/11945819/01eeca0a75aa/sensors-25-01900-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7b5/11945819/46297700d556/sensors-25-01900-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7b5/11945819/4849bc617a86/sensors-25-01900-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7b5/11945819/57607f60913d/sensors-25-01900-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7b5/11945819/bd265f780212/sensors-25-01900-g009a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7b5/11945819/b45373ff7718/sensors-25-01900-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7b5/11945819/813fea80c9aa/sensors-25-01900-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7b5/11945819/dc58073d42e1/sensors-25-01900-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7b5/11945819/b973bddfbeae/sensors-25-01900-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7b5/11945819/01eeca0a75aa/sensors-25-01900-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7b5/11945819/46297700d556/sensors-25-01900-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7b5/11945819/4849bc617a86/sensors-25-01900-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7b5/11945819/57607f60913d/sensors-25-01900-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7b5/11945819/bd265f780212/sensors-25-01900-g009a.jpg

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