一种可自适应切换的机器人导航控制方法，兼顾全局路径与局部避障。
Hybrid Feedback Control for Global Navigation with Locally Optimal Obstacle Avoidance in n-Dimensional Spaces
- 通过模式切换实现全局导航与局部避障协同
- 仿真与实测均显示路径更短、轨迹更平滑
- 兼容激光雷达等传感器，适用于未知环境
本文提出一种用于高维欧氏空间中带球形障碍物环境的自主机器人导航的混合反馈控制框架。该方法通过动态切换‘朝目标运动’与‘局部最优避障’两种模式，生成连续速度输入，确保无碰撞轨迹并实现局部最优避障。与现有方法不同，该框架兼容范围传感器，支持在已知与未知环境中导航。2D和3D仿真及TurtleBot 4平台的实验验证表明，该方法相比当前最优技术具有更短路径、更平滑轨迹，同时保持计算效率与实际可行性。
原文摘要 · Abstract (English)
We present a hybrid feedback control framework for autonomous robot navigation in n-dimensional Euclidean spaces cluttered with spherical obstacles. The proposed approach ensures safe and global navigation towards a target location by dynamically switching between two operational modes: motion-to-destination and locally optimal obstacle-avoidance. It produces continuous velocity inputs, ensures collision-free trajectories and generates locally optimal obstacle avoidance maneuvers. Unlike existing methods, the proposed framework is compatible with range sensors, enabling navigation in both a priori known and unknown environments. Extensive simulations in 2D and 3D settings, complemented by experimental validation on a TurtleBot 4 platform, confirm the efficacy and robustness of the approach. Our results demonstrate shorter paths and smoother trajectories compared to state-of-the-art methods, while maintaining computational efficiency and real-world feasibility.
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