Towards Local Minima-free Robotic Navigation: Model Predictive Path Integral Control via Repulsive Potential Augmentation

TL;DR

This paper introduces a novel motion planning approach that enhances model predictive control with repulsive potential augmentation, effectively avoiding local minima in robotic navigation without sacrificing computational efficiency.

Contribution

It proposes a new method integrating repulsive potential fields into model predictive path integral control to proactively prevent local minima entrapment.

Findings

Guarantees avoidance of local minima.

Outperforms existing methods in global optimality.

Maintains computational efficiency.

Abstract

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.