首页|Optimal Design of the Modular Joint Drive Train for Enhancing Cobot Load Capacity and Dynamic Performance

Optimal Design of the Modular Joint Drive Train for Enhancing Cobot Load Capacity and Dynamic Performance

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Automation advancements prompts the extensive integration of collaborative robot(cobot)across a range of industries.Compared to the commonly used design approach of increasing the payload-to-weight ratio of cobot to enhance load capacity,equal attention should be paid to the dynamic response characteristics of cobot dur-ing the design process to make the cobot more flexible.In this paper,a new method for designing the drive train parameters of cobot is proposed.Firstly,based on the analysis of factors influencing the load capacity and dynamic response characteristics,design criteria for both aspects are established for cobot with all optimization design criteria normalized within the design domain.Secondly,with the cobot in the horizontal pose,the motor design scheme is discretized and it takes the joint motor diameter and gearbox speed ratio as optimization design variables.Finally,all the discrete values of the optimization objectives are obtained through the enumeration method and the Pareto front is used to select the optimal solution through multi-objective optimization.Base on the cobot design method proposed in this paper,a six-axis cobot is designed and compared with the commercial cobot.The result shows that the load capacity of the designed cobot in this paper reaches 8.4 kg,surpassing the 5 kg load capacity commer-cial cobot which is used as a benchmark.The minimum resonance frequency of the joints is 42.70 Hz.

Multi-objective optimizationModular joint drive train designLoad capacityDynamic response performance

Peng Li、Zhenguo Nie、Zihao Li、Xinjun Liu

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Department of Mechanical Engineering,Tsinghua University,Beijing 100084,China

State Key Laboratory of Tribology in Advanced Equipment,Tsinghua University,Beijing 100084,China

Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control,Tsinghua University,Beijing 100084,China

National Key Research and Development Program of ChinaNational Key Research and Development Program of China

2022YFB47030002019YFB1309900

2024

10.1186/s10033-024-01041-5

中国机械工程学报
中国机械工程学会

中国机械工程学报

CSTPCD
影响因子：0.765
ISSN：1000-9345
年,卷(期)：2024.37(3)