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Increasing the energy efficiency of industrial robots in component manufacture can decrease running expenses and enhance the sustainability of robot operations. In this work, we use the boosting data-driven evolutionary algorithm with localized data generation (BDDEA-LDG) and a Bezier curve interpolator to optimize the trajectory of the robot arm manipulation. The experiment verified the UR3 robotic arm in 4 real-world cases. The test results indicated that the best result saved 7.63% of the energy, while the worst saved 2.07%, as compared to the movej command (system default). As a result, the proposed algorithm provides competitive results in optimizing the robot’s energy consumption.

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Robotic Arm Trajectory Optimization Using Data-Driven Evolutionary Algorithm

  • 会议论文

  • 发表日期：

    2026年

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  • Xiaopeng Wang,
  • Aleš Vysocký,
  • Tomáš Kot,
  • Lingping Kong,
  • Ponnuthurai Nagaratnam Suganthan,
  • Zdenko Bobovský,
  • Václav Snášel

摘要

Increasing the energy efficiency of industrial robots in component manufacture can decrease running expenses and enhance the sustainability of robot operations. In this work, we use the boosting data-driven evolutionary algorithm with localized data generation (BDDEA-LDG) and a Bezier curve interpolator to optimize the trajectory of the robot arm manipulation. The experiment verified the UR3 robotic arm in 4 real-world cases. The test results indicated that the best result saved 7.63% of the energy, while the worst saved 2.07%, as compared to the movej command (system default). As a result, the proposed algorithm provides competitive results in optimizing the robot’s energy consumption.