Multi-objective time-energy-impact optimization for robotic excavator trajectory planning
Single-objective optimal trajectory cannot adapt to the complex requirements of excavator construction. A comprehensive optimal trajectory planning method is proposed to optimize the working time, energy consumption, and operational impact of robotic excavators. Without fusing any performance indexe...
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| Published in: | Automation in construction Vol. 156; p. 105094 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.12.2023
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| ISSN: | 0926-5805, 1872-7891 |
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| Abstract | Single-objective optimal trajectory cannot adapt to the complex requirements of excavator construction. A comprehensive optimal trajectory planning method is proposed to optimize the working time, energy consumption, and operational impact of robotic excavators. Without fusing any performance indexes, a normalized multi-objective function and an improved particle swarm optimization algorithm are established to achieve a comprehensive optimization of multiple objectives, while considering joint angle, velocity, acceleration, and quadratic acceleration constraints. Typical deep pit excavation simulation and experimental results show that the multi-objective optimization method is feasible, can balance multi-objective constraints, and can avoid falling into extremely long working times or large impacts. This method offers a more efficient and effective solution for multi-objective trajectory planning and provides a method for planning excavation trajectories based on different operating scenarios and objectives.
•Working time, energy consumption and operation impact are considered in trajectory planning.•A normalized multi-objective function is established to achieve a comprehensive optimization.•An improved particle swarm optimization algorithm is proposed to obtain the optimal solution.•Effectiveness of the trajectory planning method is validated by simulations and experiments.•The multi-objective optimization method can meet the actual construction requirements. |
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| AbstractList | Single-objective optimal trajectory cannot adapt to the complex requirements of excavator construction. A comprehensive optimal trajectory planning method is proposed to optimize the working time, energy consumption, and operational impact of robotic excavators. Without fusing any performance indexes, a normalized multi-objective function and an improved particle swarm optimization algorithm are established to achieve a comprehensive optimization of multiple objectives, while considering joint angle, velocity, acceleration, and quadratic acceleration constraints. Typical deep pit excavation simulation and experimental results show that the multi-objective optimization method is feasible, can balance multi-objective constraints, and can avoid falling into extremely long working times or large impacts. This method offers a more efficient and effective solution for multi-objective trajectory planning and provides a method for planning excavation trajectories based on different operating scenarios and objectives.
•Working time, energy consumption and operation impact are considered in trajectory planning.•A normalized multi-objective function is established to achieve a comprehensive optimization.•An improved particle swarm optimization algorithm is proposed to obtain the optimal solution.•Effectiveness of the trajectory planning method is validated by simulations and experiments.•The multi-objective optimization method can meet the actual construction requirements. |
| ArticleNumber | 105094 |
| Author | Jiang, Jinye Feng, Hao Cao, Donghui Li, Chunbiao Shen, Fangping Ding, Nan Liu, Tao Xie, Jiaxue Yin, Chenbo |
| Author_xml | – sequence: 1 givenname: Hao surname: Feng fullname: Feng, Hao email: fenghao@nuist.edu.cn organization: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China – sequence: 2 givenname: Jinye surname: Jiang fullname: Jiang, Jinye organization: School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China – sequence: 3 givenname: Nan surname: Ding fullname: Ding, Nan organization: Changwang School of Honors, Nanjing University of Information Science and Technology, Nanjing 210044, China – sequence: 4 givenname: Fangping surname: Shen fullname: Shen, Fangping organization: Changwang School of Honors, Nanjing University of Information Science and Technology, Nanjing 210044, China – sequence: 5 givenname: Chenbo surname: Yin fullname: Yin, Chenbo organization: United Institute of Excavator Key Technology, Nanjing Tech University, Nanjing 211816, China – sequence: 6 givenname: Donghui surname: Cao fullname: Cao, Donghui organization: SANY Group Co., Ltd., Suzhou 215300, China – sequence: 7 givenname: Chunbiao surname: Li fullname: Li, Chunbiao organization: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China – sequence: 8 givenname: Tao surname: Liu fullname: Liu, Tao organization: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China – sequence: 9 givenname: Jiaxue surname: Xie fullname: Xie, Jiaxue organization: Wuxi Cosmo Suspended Platform Co., Ltd., Wuxi 214128, China |
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| Keywords | Excavator Trajectory planning Hydraulic system Particle swarm optimization algorithm |
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| SubjectTerms | Excavator Hydraulic system Particle swarm optimization algorithm Trajectory planning |
| Title | Multi-objective time-energy-impact optimization for robotic excavator trajectory planning |
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