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In order to solve the problems of low efficiency and serious wear and tear of the plate mounting robot, this paper proposes an enhanced genetic algorithm based on non-dominated sorting (NSGA-II), which enables the optimization of the optimal trajectory with regard to time and energy consumption for plate installation robots. First of all, establishing that forward kinematics equation of the robot, coordinated pose relationship of the robot is deduced and the dynamics analysis is carried out. Secondly, it deduces quintic B-spline curves for joint space trajectory planning. Finally, the improved NSGA-II algorithm optimizes the time-energy consumption of quintic B-spline curves, employing an advanced fuzzy evaluation method to identify potential optimal solutions in Pareto solution set and carry out experimental verification. Simulation and experimental results demonstrate quintic B-spline curve reduces energy consumption by 13% compared with quintic polynomial curve, thus validating the algorithm’s effectiveness. The research results of this paper provide a theoretical basis for other multi-objective trajectory planning.

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Optimal Trajectory Planning of Construction Robot Energy Consumption Based on Multi-objective Optimization

  • 会议论文

  • 发表日期：

    2025年

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  • Ming Han,
  • Haidong Wang,
  • Bin Xiong,
  • Chaoyang Lu,
  • Dong Yang

摘要

In order to solve the problems of low efficiency and serious wear and tear of the plate mounting robot, this paper proposes an enhanced genetic algorithm based on non-dominated sorting (NSGA-II), which enables the optimization of the optimal trajectory with regard to time and energy consumption for plate installation robots. First of all, establishing that forward kinematics equation of the robot, coordinated pose relationship of the robot is deduced and the dynamics analysis is carried out. Secondly, it deduces quintic B-spline curves for joint space trajectory planning. Finally, the improved NSGA-II algorithm optimizes the time-energy consumption of quintic B-spline curves, employing an advanced fuzzy evaluation method to identify potential optimal solutions in Pareto solution set and carry out experimental verification. Simulation and experimental results demonstrate quintic B-spline curve reduces energy consumption by 13% compared with quintic polynomial curve, thus validating the algorithm’s effectiveness. The research results of this paper provide a theoretical basis for other multi-objective trajectory planning.