Collision-Free Navigation of Mobile Robots via Quadtree-Based Model Predictive Control

TL;DR

This paper introduces a novel quadtree-based model predictive control framework for autonomous mobile robots, enabling efficient, reliable, and collision-free navigation through structured environment representation and trajectory optimization.

Contribution

It presents a unified navigation system that integrates environment mapping, safe region extraction, and MPC using quadtree structures, improving navigation safety and efficiency.

Findings

Consistent success in complex environments

Superior performance over baseline methods

Efficient collision avoidance without obstacle encoding

Abstract

This paper presents an integrated navigation framework for Autonomous Mobile Robots (AMRs) that unifies environment representation, trajectory generation, and Model Predictive Control (MPC). The proposed approach incorporates a quadtree-based method to generate structured, axis-aligned collision-free regions from occupancy maps. These regions serve as both a basis for developing safe corridors and as linear constraints within the MPC formulation, enabling efficient and reliable navigation without requiring direct obstacle encoding. The complete pipeline combines safe-area extraction, connectivity graph construction, trajectory generation, and B-spline smoothing into one coherent system. Experimental results demonstrate consistent success and superior performance compared to baseline approaches across complex environments.