融合势场与采样规划，提升机械臂在动态环境中的实时避障能力。
Overcoming Dynamic Environments: A Hybrid Approach to Motion Planning for Manipulators
- 用改进的势场法结合采样规划，兼顾实时性与路径最优性。
- 在复杂场景中避免局部极小点，路径平滑性与稳定性显著提升。
- 适合仓储、手术机器人等需快速响应动态障碍的场景。
在动态不确定环境中运行的机器人机械臂需要高效的运动规划以避开障碍物并保持轨迹平滑。速度势场（VPF）规划器具备实时适应性，但在复杂约束和局部极小值问题上表现不佳，导致在杂乱空间中性能下降。传统方法依赖预规划轨迹，但频繁重规划计算成本高。本文提出一种混合运动规划方法，将改进的VPF与基于采样的运动规划器（SBMP）结合。SBMP保证路径最优性，而VPF提供对动态障碍的实时响应能力。该方案提升了运动规划的效率、稳定性与计算可行性，有效应对仓储、外科机器人等不确定环境中的关键挑战。
原文摘要 · Abstract (English)
Robotic manipulators operating in dynamic and uncertain environments require efficient motion planning to navigate obstacles while maintaining smooth trajectories. Velocity Potential Field (VPF) planners offer real-time adaptability but struggle with complex constraints and local minima, leading to suboptimal performance in cluttered spaces. Traditional approaches rely on pre-planned trajectories, but frequent recomputation is computationally expensive. This study proposes a hybrid motion planning approach, integrating an improved VPF with a Sampling-Based Motion Planner (SBMP). The SBMP ensures optimal path generation, while VPF provides real-time adaptability to dynamic obstacles. This combination enhances motion planning efficiency, stability, and computational feasibility, addressing key challenges in uncertain environments such as warehousing and surgical robotics.
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