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Multi-objective time-energy-impact optimization for robotic excavator trajectory planning
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.
Highlights 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.
Multi-objective time-energy-impact optimization for robotic excavator trajectory planning
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.
Highlights 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.
Multi-objective time-energy-impact optimization for robotic excavator trajectory planning
Feng, Hao (Autor:in) / Jiang, Jinye (Autor:in) / Ding, Nan (Autor:in) / Shen, Fangping (Autor:in) / Yin, Chenbo (Autor:in) / Cao, Donghui (Autor:in) / Li, Chunbiao (Autor:in) / Liu, Tao (Autor:in) / Xie, Jiaxue (Autor:in)
12.09.2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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