Renluan Hou, Jianwei Niu, Yuliang Guo, Tao Ren, Bing Han, Xiaolong Yu, Qun Ma, J. Wang, Renjie Qi
{"title":"基于可靠动态辨识的定制化工业机器人多目标轨迹优化规划,提高控制精度","authors":"Renluan Hou, Jianwei Niu, Yuliang Guo, Tao Ren, Bing Han, Xiaolong Yu, Qun Ma, J. Wang, Renjie Qi","doi":"10.1108/ir-12-2021-0301","DOIUrl":null,"url":null,"abstract":"\nPurpose\nThe purpose of this paper is to enhance control accuracy, energy efficiency and productivity of customized industrial robots by the proposed multi-objective trajectory optimization approach. To obtain accurate dynamic matching torques of the robot joints with optimal motion, an improved dynamic model built by a novel parameter identification method has been proposed.\n\n\nDesign/methodology/approach\nThis paper proposes a novel multi-objective optimal approach to minimize the time and energy consumption of robot trajectory. First, the authors develop a reliable dynamic parameters identification method to obtain joint torques for formulating the normalized energy optimization function and dynamic constraints. Then, optimal trajectory variables are solved by converting the objective function into relaxation constraints based on second-order cone programming and Runge–Kutta discrete method to reduce the solving complexity.\n\n\nFindings\nExtensive experiments via simulation and in real customized robots are conducted. The results of this paper illustrate that the accuracy of joint torque predicted by the proposed model increases by 28.79% to 79.05% over the simplified models used in existing optimization studies. Meanwhile, under the same solving efficiency, the proposed optimization trajectory consumes a shorter time and less energy compared with the existing optimization ones and the polynomial trajectory.\n\n\nOriginality/value\nA novel time-energy consumption optimal trajectory planning method based on dynamic identification is proposed. Most existing optimization methods neglect the effect of dynamic model reliability on energy efficiency optimization. A novel parameter identification approach and a complete dynamic torque model are proposed. Experimental results of dynamic matching torques verify that the control accuracy of optimal robot motion can be significantly improved by the proposed model.\n","PeriodicalId":54987,"journal":{"name":"Industrial Robot-The International Journal of Robotics Research and Application","volume":"7 1","pages":"1156-1168"},"PeriodicalIF":1.9000,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Multi-objective optimal trajectory planning of customized industrial robot based on reliable dynamic identification for improving control accuracy\",\"authors\":\"Renluan Hou, Jianwei Niu, Yuliang Guo, Tao Ren, Bing Han, Xiaolong Yu, Qun Ma, J. Wang, Renjie Qi\",\"doi\":\"10.1108/ir-12-2021-0301\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nPurpose\\nThe purpose of this paper is to enhance control accuracy, energy efficiency and productivity of customized industrial robots by the proposed multi-objective trajectory optimization approach. To obtain accurate dynamic matching torques of the robot joints with optimal motion, an improved dynamic model built by a novel parameter identification method has been proposed.\\n\\n\\nDesign/methodology/approach\\nThis paper proposes a novel multi-objective optimal approach to minimize the time and energy consumption of robot trajectory. First, the authors develop a reliable dynamic parameters identification method to obtain joint torques for formulating the normalized energy optimization function and dynamic constraints. Then, optimal trajectory variables are solved by converting the objective function into relaxation constraints based on second-order cone programming and Runge–Kutta discrete method to reduce the solving complexity.\\n\\n\\nFindings\\nExtensive experiments via simulation and in real customized robots are conducted. The results of this paper illustrate that the accuracy of joint torque predicted by the proposed model increases by 28.79% to 79.05% over the simplified models used in existing optimization studies. Meanwhile, under the same solving efficiency, the proposed optimization trajectory consumes a shorter time and less energy compared with the existing optimization ones and the polynomial trajectory.\\n\\n\\nOriginality/value\\nA novel time-energy consumption optimal trajectory planning method based on dynamic identification is proposed. Most existing optimization methods neglect the effect of dynamic model reliability on energy efficiency optimization. A novel parameter identification approach and a complete dynamic torque model are proposed. 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Multi-objective optimal trajectory planning of customized industrial robot based on reliable dynamic identification for improving control accuracy
Purpose
The purpose of this paper is to enhance control accuracy, energy efficiency and productivity of customized industrial robots by the proposed multi-objective trajectory optimization approach. To obtain accurate dynamic matching torques of the robot joints with optimal motion, an improved dynamic model built by a novel parameter identification method has been proposed.
Design/methodology/approach
This paper proposes a novel multi-objective optimal approach to minimize the time and energy consumption of robot trajectory. First, the authors develop a reliable dynamic parameters identification method to obtain joint torques for formulating the normalized energy optimization function and dynamic constraints. Then, optimal trajectory variables are solved by converting the objective function into relaxation constraints based on second-order cone programming and Runge–Kutta discrete method to reduce the solving complexity.
Findings
Extensive experiments via simulation and in real customized robots are conducted. The results of this paper illustrate that the accuracy of joint torque predicted by the proposed model increases by 28.79% to 79.05% over the simplified models used in existing optimization studies. Meanwhile, under the same solving efficiency, the proposed optimization trajectory consumes a shorter time and less energy compared with the existing optimization ones and the polynomial trajectory.
Originality/value
A novel time-energy consumption optimal trajectory planning method based on dynamic identification is proposed. Most existing optimization methods neglect the effect of dynamic model reliability on energy efficiency optimization. A novel parameter identification approach and a complete dynamic torque model are proposed. Experimental results of dynamic matching torques verify that the control accuracy of optimal robot motion can be significantly improved by the proposed model.
期刊介绍:
Industrial Robot publishes peer reviewed research articles, technology reviews and specially commissioned case studies. Each issue includes high quality content covering all aspects of robotic technology, and reflecting the most interesting and strategically important research and development activities from around the world.
The journal’s policy of not publishing work that has only been tested in simulation means that only the very best and most practical research articles are included. This ensures that the material that is published has real relevance and value for commercial manufacturing and research organizations. Industrial Robot''s coverage includes, but is not restricted to:
Automatic assembly
Flexible manufacturing
Programming optimisation
Simulation and offline programming
Service robots
Autonomous robots
Swarm intelligence
Humanoid robots
Prosthetics and exoskeletons
Machine intelligence
Military robots
Underwater and aerial robots
Cooperative robots
Flexible grippers and tactile sensing
Robot vision
Teleoperation
Mobile robots
Search and rescue robots
Robot welding
Collision avoidance
Robotic machining
Surgical robots
Call for Papers 2020
AI for Autonomous Unmanned Systems
Agricultural Robot
Brain-Computer Interfaces for Human-Robot Interaction
Cooperative Robots
Robots for Environmental Monitoring
Rehabilitation Robots
Wearable Robotics/Exoskeletons.