Minimum Energy Trajectory Optimization for Driving Systems of Palletizing Robot Joints
Palletizing robot is widely used in logistics operation. At present, people pay attention to not only the loading capacity and working efficiency of palletizing robots, but also the energy consumption in their working process. This paper takes MD1200-YJ palletizing robot as the research object and s...
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2018-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2018/7247093 |
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doaj-ddc51b3440a342c28008cd1b676bda1c2020-11-25T00:05:31ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472018-01-01201810.1155/2018/72470937247093Minimum Energy Trajectory Optimization for Driving Systems of Palletizing Robot JointsYing He0Jiangping Mei1Zhiwei Fang2Fan Zhang3Yanqin Zhao4Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300072, ChinaPalletizing robot is widely used in logistics operation. At present, people pay attention to not only the loading capacity and working efficiency of palletizing robots, but also the energy consumption in their working process. This paper takes MD1200-YJ palletizing robot as the research object and studies the problem of low energy consumption optimization of joint driving system from the perspective of trajectory optimization. Firstly, a multifactor dynamic model of palletizing robot is established based on the conventional inverse rigid body dynamic model of the robot, the Stribeck friction model and the spring balance torque model. And then based on joint torque, friction torque, motion parameter, and joule’s law, the useful work model, thermal loss model of joint motor, friction energy consumption model of joint system, and total energy consumption model of palletizing robot are established, and through simulation and experiment, the correctness of the multifactor dynamic model and energy consumption model is verified. Secondly, based on the Fourier series approximation method to construct the joint trajectory expression, the minimum total energy consumption as the optimization objective, with coefficients of Fourier series as optimization variables, the motion parameters of initial and final position, and running time constant as constraint conditions, the genetic algorithm is used to solve the optimization problem. Finally, through the simulation analysis the optimized Fourier series motion law and the 3-4-5 polynomial motion law are comprehensively evaluated to verify the effectiveness of the optimization method. Moreover, it provides the theoretical basis for the follow-up research and points out the direction of improvement.http://dx.doi.org/10.1155/2018/7247093 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ying He Jiangping Mei Zhiwei Fang Fan Zhang Yanqin Zhao |
spellingShingle |
Ying He Jiangping Mei Zhiwei Fang Fan Zhang Yanqin Zhao Minimum Energy Trajectory Optimization for Driving Systems of Palletizing Robot Joints Mathematical Problems in Engineering |
author_facet |
Ying He Jiangping Mei Zhiwei Fang Fan Zhang Yanqin Zhao |
author_sort |
Ying He |
title |
Minimum Energy Trajectory Optimization for Driving Systems of Palletizing Robot Joints |
title_short |
Minimum Energy Trajectory Optimization for Driving Systems of Palletizing Robot Joints |
title_full |
Minimum Energy Trajectory Optimization for Driving Systems of Palletizing Robot Joints |
title_fullStr |
Minimum Energy Trajectory Optimization for Driving Systems of Palletizing Robot Joints |
title_full_unstemmed |
Minimum Energy Trajectory Optimization for Driving Systems of Palletizing Robot Joints |
title_sort |
minimum energy trajectory optimization for driving systems of palletizing robot joints |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2018-01-01 |
description |
Palletizing robot is widely used in logistics operation. At present, people pay attention to not only the loading capacity and working efficiency of palletizing robots, but also the energy consumption in their working process. This paper takes MD1200-YJ palletizing robot as the research object and studies the problem of low energy consumption optimization of joint driving system from the perspective of trajectory optimization. Firstly, a multifactor dynamic model of palletizing robot is established based on the conventional inverse rigid body dynamic model of the robot, the Stribeck friction model and the spring balance torque model. And then based on joint torque, friction torque, motion parameter, and joule’s law, the useful work model, thermal loss model of joint motor, friction energy consumption model of joint system, and total energy consumption model of palletizing robot are established, and through simulation and experiment, the correctness of the multifactor dynamic model and energy consumption model is verified. Secondly, based on the Fourier series approximation method to construct the joint trajectory expression, the minimum total energy consumption as the optimization objective, with coefficients of Fourier series as optimization variables, the motion parameters of initial and final position, and running time constant as constraint conditions, the genetic algorithm is used to solve the optimization problem. Finally, through the simulation analysis the optimized Fourier series motion law and the 3-4-5 polynomial motion law are comprehensively evaluated to verify the effectiveness of the optimization method. Moreover, it provides the theoretical basis for the follow-up research and points out the direction of improvement. |
url |
http://dx.doi.org/10.1155/2018/7247093 |
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