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

2025-11-17

AI Summary

To address collision-free navigation for autonomous mobile robots in complex environments, this paper proposes a navigation framework integrating structured environmental representation with model predictive control (MPC). The method first adaptively partitions the occupancy map using a quadtree to extract axis-aligned safe regions; a connectivity graph is then constructed to generate an initial path, which is subsequently smoothed via B-spline interpolation. Crucially, these safe regions are directly encoded as linear constraints in the MPC optimization—eliminating the need for explicit obstacle modeling. This enables tight coupling between path planning and motion control, achieving joint optimization of safety and computational efficiency. Experimental results demonstrate that the approach ensures stable, real-time, collision-free navigation across diverse high-density and unstructured scenarios, consistently outperforming state-of-the-art baseline methods in both robustness and runtime performance.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Natural Language Processing: Safety and Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Developing collision-free navigation for mobile robots using quadtree-based environment representation
• Integrating trajectory generation and Model Predictive Control with linear safety constraints
• Enabling efficient autonomous navigation without direct obstacle encoding in complex environments

Innovation

• Quadtree-based method generates collision-free regions
• Model Predictive Control uses linear constraints for navigation
• Integrated pipeline combines safe corridors with trajectory smoothing