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.
翻译：本文提出了一种用于自主移动机器人的集成导航框架，该框架将环境表示、轨迹生成与模型预测控制统一起来。所提出的方法采用基于四叉树的方法，从占据地图中生成结构化的、轴对齐的无碰撞区域。这些区域既作为构建安全走廊的基础，也作为MPC公式中的线性约束，从而无需直接编码障碍物即可实现高效可靠的导航。完整的流程将安全区域提取、连通图构建、轨迹生成与B样条平滑整合为一个连贯的系统。实验结果表明，在复杂环境中，该方法相比基线方法具有持续的成功率和更优的性能。
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