使用示范学习与动态调制增强可变形环境中的机器人导航
Enhanced Robotic Navigation in Deformable Environments using Learning from Demonstration and Dynamic Modulation
AI总结：
本文提出一种结合示范学习与动态系统的机器人导航新方法，通过引入动态调制矩阵实时区分软硬区域，实现可变形环境下的安全灵活轨迹规划与速度控制。
摘要：
本文提出了一种在包含可变形障碍物的环境中进行机器人导航的新方法。通过将示范学习与动态系统相结合，我们实现了在由软硬区域组成障碍物的复杂环境中的自适应高效导航。我们在DS框架内引入了动态调制矩阵，使系统能够实时区分可穿越的软区域和不可通过的硬区域，确保安全灵活的轨迹规划。我们通过大量仿真和机器人实验验证了该方法，展示了其导航可变形环境的能力。此外，该方法在与可变形物体交互（包括在交叉路口）时能同时控制轨迹和速度，同时保持对原始DS轨迹的遵循并动态适应障碍物，实现平滑可靠的导航。
英文摘要：
This paper presents a novel approach for robot navigation in environments containing deformable obstacles. By integrating Learning from Demonstration (LfD) with Dynamical Systems (DS), we enable adaptive and efficient navigation in complex environments where obstacles consist of both soft and hard regions. We introduce a dynamic modulation matrix within the DS framework, allowing the system to distinguish between traversable soft regions and impassable hard areas in real-time, ensuring safe and flexible trajectory planning. We validate our method through extensive simulations and robot experiments, demonstrating its ability to navigate deformable environments. Additionally, the approach provides control over both trajectory and velocity when interacting with deformable objects, including at intersections, while maintaining adherence to the original DS trajectory and dynamically adapting to obstacles for smooth and reliable navigation.
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。
