Scholarlink

This paper introduces a practical Online Parallel Optimization Motion Planner (OPOMP) designed for robots operating in dynamic environments, integrating sampling-based motion planning with optimization-based online trajectory generation. While sampling-based methods efficiently find feasible paths in high-dimensional configuration spaces, they struggle with optimizing paths quickly. As robots increasingly operate in dynamic environments, effective replanning algorithms are crucial for responding to moving obstacles. The OPOMP framework includes the Progressive Rapidly-exploring Random Tree \(^*\) (PRRT \(^*\) ) for initial path optimization and Piecewise Particle Swarm Optimization (PPSO) for generating time-optimal trajectories. The framework has been validated through simulations on a 6 DOF manipulator, demonstrating its effectiveness and robustness.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Online Parallel Optimization Motion Planner for Robots Under Moving Obstacle Circumstances

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Chengzhi Wang,
  • Shize Zhao,
  • Tianjiao Zheng,
  • Hegao Cai,
  • Yanhe Zhu

摘要

This paper introduces a practical Online Parallel Optimization Motion Planner (OPOMP) designed for robots operating in dynamic environments, integrating sampling-based motion planning with optimization-based online trajectory generation. While sampling-based methods efficiently find feasible paths in high-dimensional configuration spaces, they struggle with optimizing paths quickly. As robots increasingly operate in dynamic environments, effective replanning algorithms are crucial for responding to moving obstacles. The OPOMP framework includes the Progressive Rapidly-exploring Random Tree \(^*\) (PRRT \(^*\) ) for initial path optimization and Piecewise Particle Swarm Optimization (PPSO) for generating time-optimal trajectories. The framework has been validated through simulations on a 6 DOF manipulator, demonstrating its effectiveness and robustness.