摘要
目前连续型机器人负载能力较弱,不能满足大负载的应用需求,设计了一种基于分布式弹性单元能够承受较大负载的绳驱动十字节连接连续型机器人,而且该机器人具备被动柔顺性,可以用于缓冲、储能等场合。为了分析该连续型机器人的弯曲变形与所受负载之间的静力学关系,建立了有负载条件下的牛顿-欧拉方程,并通过数值求解器进行计算模拟,与经典的常曲率模型假设计算结果对比,牛顿-欧拉方程的仿真结果更符合实际变形量。对连续型机器人末端进行了水平、竖直、圆周运动三组实验,实验结果表明,在7.5 kg外部负载的作用下,该机器人分隔盘边缘点与对应仿真计算点的位置平均误差最大为9.62 mm,均方误差为4.69 mm,分别占连续型机器人总长的4.20%与2.04%,表明该机器人能够实现大负载作用下的精确运动,验证了机器人承担大负载的能力。
Currently,continuum robots have weak load capacity and cannot meet the application requirements of large loads.Therefore,a cable-driven continuum robot based on distributed elastic elements that can withstand large loads is designed.The robot has passive compliance and can be utilized for applications such as cushioning,energy saving condition.In order to build the static model between the bending deformation of the continuum robot and the external load,Newton-Euler equations under external loads are established,and numerical solvers are designed for simulation.Compared with the classical constant curvature model,the simulation results are more consistent with the actual deformation.Three groups of experiments are conducted for horizontal,vertical,and circular motions at the end of the continuum robot.The results show that under a 7.5 kg load,the maximum average error between the edge points of the robot disks and corresponding simulation points is 6.58 mm,and the mean square error is 4.50 mm.These values respectively account for 2.87%and 1.96%of the total length of the continuum robot,indicating that the robot can achieve accurate motion under large loads and verifying its feasibility in large loads.
作者
雷飞
刘思宇
廖峻北
郭朝
王志瑞
闫曈
党睿娜
苏波
LEI Fei;LIU Siyu;LIAO Junbei;GUO Zhao;WANG Zhirui;YAN Tong;DANG Ruina;SU Bo(School of Power and Mechanical Engineering,Wuhan University,Wuhan 430072;China North Artificial Intelligence&Innovation Research Institute,Beijing 100072;Collective Intelligence&Collaboration Laboratory,Beijing 100072)
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2024年第15期28-37,共10页
Journal of Mechanical Engineering
基金
国家重点研发计划(2023YFE0202100)
国家自然科学青年基金(51605339)
群体协同与自主实验室开放基金课题(QXZ23013101)资助项目。