提出多向安全矩形走廊MPC，提升复杂环境导航效率与安全性
Multi-directional Safe Rectangle Corridor-Based MPC for Nonholonomic Robots Navigation in Cluttered Environment
- 用多向矩形走廊编码自由空间，减少计算负担
- 相比传统方法，走廊面积平均提升41.05%，生成延迟仅3毫秒
- 适合工业移动机器人在密集环境中实时避障导航
自主移动机器人（AMRs）因其操作灵活性和高效性，在工业应用中日益不可或缺。导航是完成复杂任务的关键技术基础。然而，在密集、杂乱且半结构化的环境中导航仍具挑战，主要源于非完整车辆动力学、与静态/动态障碍物的交互，以及操作空间的非凸约束特性。为此，本文提出一种改进的顺序模型预测控制（ISMPC）导航框架，将导航任务系统性地重构为一系列切换最优控制问题。通过两项关键创新解决上述问题：1）实现多向安全矩形走廊（MDSRC）算法，通过矩形凸区域编码自由空间，避免与静态障碍物碰撞，消除冗余计算负担并加速求解器收敛；2）提出融合走廊约束与屏障函数约束的顺序MPC导航框架，实现对静态与动态障碍物的有效避让。该框架可直接生成速度指令，简化传统导航算法架构。对比实验表明，该框架在自由空间利用率上显著提升（平均走廊面积增加41.05%），同时保持实时计算性能（平均走廊生成延迟为3毫秒）。
原文摘要 · Abstract (English)
Autonomous Mobile Robots (AMRs) have become indispensable in industrial applications due to their operational flexibility and efficiency. Navigation serves as a crucial technical foundation for accomplishing complex tasks. However, navigating AMRs in dense, cluttered, and semi-structured environments remains challenging, primarily due to nonholonomic vehicle dynamics, interactions with mixed static/dynamic obstacles, and the non-convex constrained nature of such operational spaces. To solve these problems, this paper proposes an Improved Sequential Model Predictive Control (ISMPC) navigation framework that systematically reformulates navigation tasks as sequential switched optimal control problems. The framework addresses the aforementioned challenges through two key innovations: 1) Implementation of a Multi-Directional Safety Rectangular Corridor (MDSRC) algorithm, which encodes the free space through rectangular convex regions to avoid collision with static obstacles, eliminating redundant computational burdens and accelerating solver convergence; 2) A sequential MPC navigation framework that integrates corridor constraints with barrier function constraints is proposed to achieve static and dynamic obstacle avoidance. The ISMPC navigation framework enables direct velocity generation for AMRs, simplifying traditional navigation algorithm architectures. Comparative experiments demonstrate the framework's superiority in free-space utilization ( an increase of 41.05$\%$ in the average corridor area) while maintaining real-time computational performance (average corridors generation latency of 3 ms).
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