用四叉树生成安全路径，让机器人在复杂环境里自动避障
Collision-Free Navigation of Mobile Robots via Quadtree-Based Model Predictive Control
- 用四叉树从地图中提取规则的无碰撞区域
- 构建安全走廊并作为MPC的线性约束，提升导航效率
- 适合需要高可靠避障的移动机器人系统
本文提出一种集成式自主移动机器人（AMR）导航框架，统一环境表示、轨迹生成与模型预测控制（MPC）。该方法采用基于四叉树的结构，从占据地图中生成轴对齐的无碰撞区域，这些区域既用于构建安全通道，也作为MPC中的线性约束，实现无需直接编码障碍物的高效可靠导航。完整流程包括安全区提取、连通图构建、轨迹生成与B样条平滑，形成一体化系统。实验表明，在复杂环境中该方法均保持稳定成功，性能优于基线方案。
原文摘要 · Abstract (English)
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.
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