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<p>To address the challenges of environmental and collaborative obstacle avoidance in dual-arm robot path optimization, a time-optimal trajectory optimization method based on a multi-strategy optimized Whale Optimization Algorithm (WOA) is proposed. First, the left arm is selected as the primary manipulator for path planning within the workspace, with its trajectory treated as an obstacle, thus enabling path optimization for the right arm. In this process, a collision detection mechanism is introduced, converting the path optimization problem into a constrained objective optimization problem. Subsequently, the multi-strategy optimized Whale Optimization Algorithm (WOA) is employed to solve the problem, yielding the optimized path trajectory for the dual-arm robot. Finally, the method’s effectiveness is validated through simulation experiments. Simulation results demonstrate that, compared to the traditional Whale Optimization Algorithm, the proposed method reduces the total movement distance of the manipulator by 17.59% and enhances path optimization efficiency by 51%.</p>

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Improved whale optimization for time-optimal and collision-free trajectory planning in dual-arm robots

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月7日

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  • Ying Du,
  • Chengbo Xu,
  • Yunjia Wang,
  • Yupeng Liu,
  • Yuehui Wang,
  • Jian Jin

摘要

To address the challenges of environmental and collaborative obstacle avoidance in dual-arm robot path optimization, a time-optimal trajectory optimization method based on a multi-strategy optimized Whale Optimization Algorithm (WOA) is proposed. First, the left arm is selected as the primary manipulator for path planning within the workspace, with its trajectory treated as an obstacle, thus enabling path optimization for the right arm. In this process, a collision detection mechanism is introduced, converting the path optimization problem into a constrained objective optimization problem. Subsequently, the multi-strategy optimized Whale Optimization Algorithm (WOA) is employed to solve the problem, yielding the optimized path trajectory for the dual-arm robot. Finally, the method’s effectiveness is validated through simulation experiments. Simulation results demonstrate that, compared to the traditional Whale Optimization Algorithm, the proposed method reduces the total movement distance of the manipulator by 17.59% and enhances path optimization efficiency by 51%.