用斥力势场让机器人导航自动避开局部陷阱，不依赖全局路径规划。
Towards Local Minima-free Robotic Navigation: Model Predictive Path Integral Control via Repulsive Potential Augmentation
- 在模型预测路径积分中加入人工斥力势场，主动引导避障。
- 理论与仿真均证明能100%避免局部极小，且计算效率不下降。
- 适合需要高效可靠路径规划的自主机器人系统使用。
基于模型的控制是机器人导航的关键组件，但其作为有限、短视优化过程的特性，常导致陷入局部极小。以往方法虽尝试解决该问题，却要么因反应式策略牺牲解的质量，要么因生成显式路径而降低计算效率。为此，我们提出一种运动规划方法，无需依赖全局路径即可主动规避局部极小。核心思想是斥力势场增强：通过人工势场将高层方向信息整合为单一斥力项，注入模型预测路径积分控制中。我们在存在引发局部极小障碍物的环境中进行理论分析与仿真评估。结果表明，该方法可保证局部极小的完全规避，在全局最优性上优于现有方法，且未降低计算效率。
原文摘要 · Abstract (English)
Model-based control is a crucial component of robotic navigation. However, it often struggles with entrapment in local minima due to its inherent nature as a finite, myopic optimization procedure. Previous studies have addressed this issue but sacrificed either solution quality due to their reactive nature or computational efficiency in generating explicit paths for proactive guidance. To this end, we propose a motion planning method that proactively avoids local minima without any guidance from global paths. The key idea is repulsive potential augmentation, integrating high-level directional information into the Model Predictive Path Integral control as a single repulsive term through an artificial potential field. We evaluate our method through theoretical analysis and simulations in environments with obstacles that induce local minima. Results show that our method guarantees the avoidance of local minima and outperforms existing methods in terms of global optimality without decreasing computational efficiency.
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