n维空间中具有局部最优避障的全局导航混合反馈控制
Hybrid Feedback Control for Global Navigation with Locally Optimal Obstacle Avoidance in n-Dimensional Spaces
AI总结：
提出一种混合反馈控制框架，通过动态切换向目标运动和局部最优避障两种模式，实现在n维球形障碍物环境中的安全全局导航，生成连续速度输入，兼容距离传感器，仿真与实验验证了其路径更短、轨迹更平滑。
摘要：
我们提出了一种混合反馈控制框架，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。所提出的方法通过在两种运行模式之间动态切换，即向目标运动模式和局部最优避障模式，确保安全且全局地导航至目标位置。该方法产生连续的速度输入，确保无碰撞轨迹，并生成局部最优的避障机动。与现有方法不同，所提出的框架与距离传感器兼容，使得在先验已知和未知环境中均可进行导航。在2D和3D设置中的大量仿真，辅以在TurtleBot 4平台上的实验验证，证实了该方法的有效性和鲁棒性。我们的结果表明，与最先进的方法相比，所提出的方法生成了更短的路径和更平滑的轨迹，同时保持了计算效率和实际可行性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
