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In this paper, a new multi-objective functions comprising of squared values of joint jerk, acceleration, torque rate and total travel time subjected to kinematic and dynamic constraints have been formulated for achieving optimal trajectory for industrial applications. Then four different multi-objective optimization algorithms—the multi-objective particle swarm optimization technique (MOPSO), the multi-objective genetic algorithm (MOGA), non-dominated sorting genetic algorithm-II (NSGA-II) and the proposed multi-objective enhanced teaching learning-based optimization (MOETLBO)—have been utilized to obtain the optimal solution for trajectory planning. Finally, the experimental validation of the proposed technique and the summarization of simulation results have been done as a comparative study of the four different metaheuristic techniques for pick and place application of KR6 R900 KUKA industrial manipulator.

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Path Planning of KR6 R900 Vision Sensor Assisted KUKA Industrial Robot for Pick and Place Application

  • 会议论文

  • 发表日期：

    2026年

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  • Amruta Rout,
  • Kiran Kavalli,
  • Ravikumar Ramegowda

摘要

In this paper, a new multi-objective functions comprising of squared values of joint jerk, acceleration, torque rate and total travel time subjected to kinematic and dynamic constraints have been formulated for achieving optimal trajectory for industrial applications. Then four different multi-objective optimization algorithms—the multi-objective particle swarm optimization technique (MOPSO), the multi-objective genetic algorithm (MOGA), non-dominated sorting genetic algorithm-II (NSGA-II) and the proposed multi-objective enhanced teaching learning-based optimization (MOETLBO)—have been utilized to obtain the optimal solution for trajectory planning. Finally, the experimental validation of the proposed technique and the summarization of simulation results have been done as a comparative study of the four different metaheuristic techniques for pick and place application of KR6 R900 KUKA industrial manipulator.