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.

翻译：本文提出一种用于在布满球形障碍物的n维欧几里得空间中实现自主机器人导航的混合反馈控制框架。该框架通过动态切换两种操作模式——目标趋近模式与局部最优避障模式——确保系统在朝向目标位置的过程中实现安全、全局的导航。该方法能生成连续的速度输入，保证轨迹无碰撞，并产生局部最优的避障机动。与现有方法不同，所提框架兼容距离传感器，使其能在先验已知与未知环境中实现导航。在二维与三维场景中进行的大量仿真，以及在TurtleBot 4平台上的实验验证，共同证实了该方法的有效性与鲁棒性。研究结果表明，相较于现有先进方法，本框架能生成更短的路径与更平滑的轨迹，同时保持计算效率与实际可行性。