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<p>Multi-waypoint planning is essential for manipulators to execute complex tasks efficiently. It requires the manipulator to swiftly traverse designated waypoints while adhering to all the kinematic constraints. However, previous research has not fully exploited the kinematic capabilities of manipulators and has overlooked collision considerations. To address these problems, this paper presents two novel methods called recursive intermediate state optimization (RISO) and overall intermediate state optimization (OISO). RISO decomposes the multi-waypoint planning problem into a sequence of one-waypoint planning tasks, employing a recursive approach to generate feasible solutions efficiently. OISO utilizes an improved whale optimization algorithm (WOA), incorporating multiple iterative processes to perform secondary optimization in high-dimensional space based on the initial value, yielding better solutions. The proposed methods have been validated on a six-degree-of-freedom manipulator platform and compared with several traditional algorithms, as well as the state-of-the-art algorithm, Ruckig. The results show that RISO outperforms Ruckig in scenarios where computation time is critical, while OISO is better suited for scenarios where trajectory quality is prioritized over time. Furthermore, the proposed methods can also handle collisions, ensuring the generation of collision-free trajectories.</p>

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Novel multi-waypoint planning methods based on intermediate state optimization

  • 期刊论文

  • 发表日期：

    2025年2月10日

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  • Xinyu Qin,
  • Guangbao Zhao,
  • Zixuan Liao,
  • Chao Liu,
  • Zhenhua Xiong

摘要

Multi-waypoint planning is essential for manipulators to execute complex tasks efficiently. It requires the manipulator to swiftly traverse designated waypoints while adhering to all the kinematic constraints. However, previous research has not fully exploited the kinematic capabilities of manipulators and has overlooked collision considerations. To address these problems, this paper presents two novel methods called recursive intermediate state optimization (RISO) and overall intermediate state optimization (OISO). RISO decomposes the multi-waypoint planning problem into a sequence of one-waypoint planning tasks, employing a recursive approach to generate feasible solutions efficiently. OISO utilizes an improved whale optimization algorithm (WOA), incorporating multiple iterative processes to perform secondary optimization in high-dimensional space based on the initial value, yielding better solutions. The proposed methods have been validated on a six-degree-of-freedom manipulator platform and compared with several traditional algorithms, as well as the state-of-the-art algorithm, Ruckig. The results show that RISO outperforms Ruckig in scenarios where computation time is critical, while OISO is better suited for scenarios where trajectory quality is prioritized over time. Furthermore, the proposed methods can also handle collisions, ensuring the generation of collision-free trajectories.