Robotics & Machine Learning Daily News2024,Issue(Jun.5) :103-103.

Study Findings on Robotics Reported by Researchers at Harbin Institute of Techno logy (An obstacle-avoidance inverse kinematics method for robotic manipulator in overhead multi-line environment)

哈尔滨工业大学机器人学研究成果(架空多线环境下机械手避障逆运动学方法)

Robotics & Machine Learning Daily News2024,Issue(Jun.5) :103-103.

Study Findings on Robotics Reported by Researchers at Harbin Institute of Techno logy (An obstacle-avoidance inverse kinematics method for robotic manipulator in overhead multi-line environment)

哈尔滨工业大学机器人学研究成果(架空多线环境下机械手避障逆运动学方法)

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摘要

由一位新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-关于机器人的新研究结果已经公布。根据《中华人民共和国黑龙江消息》,NewsRx记者报道,研究表明:“逆运动学问题在机器人机械手规划、自主控制和物体抓取中起着至关重要的作用,该问题可以在简单的环境中通过不断的研究得到解决。”本研究的资助单位包括国家自然科学基金、湖北电力研究所、云南电网有限公司。我们的新闻记者从哈尔滨工业大学的研究中得到一句话:“然而,当需要避障时,快速找到可行的逆运动学解仍然是一个挑战。”本文提出了一种求解有避障要求的机器人逆运动学问题的非凸复合规划方法,该方法直接计算机器人与高空环境之间的最小距离,从而实现有效的避障,并基于指数积模型构造末端执行器误差函数,给出了误差函数的梯度公式,并基于几何参数方程推导出了机器人与高空环境之间的最小距离。提出了一种改进的基于短时梯度信息的自适应矩估计算法,以提高优化性能。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on robotics have bee n published. According to news originating from Heilongjiang, People’s Republic of China, by NewsRx correspondents, research stated, “The inverse kinematics pro blem plays a crucial role in robotic manipulator planning, autonomous control, a nd object grasping. This problem can be solved in simple environments based on e xisting studies.” Funders for this research include National Natural Science Foundation of China; Hubei Electric Power Research Institute; Yunnan Power Grid Co. Ltd.. Our news correspondents obtained a quote from the research from Harbin Institute of Technology: “However, it is still challenging to quickly find a feasible inv erse kinematic solution when obstacle avoidance is required. In this paper, we p resent a nonconvex composite programming method to solve the inverse kinematics problem with overhead obstacle-avoidance requirements. Our method enables effici ent obstacle avoidance by directly calculating the minimum distance between the manipulator and the overhead environment. We construct end-effector error functi ons based on the Product of Exponentials model and explicitly provide their grad ient formula. We derive the minimum distance based on the geometry parametric eq uation and directly utilize it to construct the obstacle avoidance function. We propose an enhanced version of adaptive moment estimation based on short-time gr adient information to improve optimization performance.”

Key words

Harbin Institute of Technology/Heilongj iang/People’s Republic of China/Asia/Emerging Technologies/Inverse Kinematic s/Machine Learning/Robotics/Robots

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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