让机器人在可变形障碍物中智能导航，实时区分可走与不可走区域。
Enhanced Robotic Navigation in Deformable Environments using Learning from Demonstration and Dynamic Modulation
- 结合示范学习与动态系统，实现对软硬障碍物的实时识别与适应
- 通过动态调制矩阵支持轨迹与速度双重控制，确保路径安全可靠
- 适用于复杂交互场景，适合需要灵活避障的机器人应用
本文提出一种新型机器人导航方法，用于包含可变形障碍物的环境。通过将示范学习（LfD）与动态系统（DS）相结合，实现了在复杂环境中对软硬区域的自适应、高效导航。我们在DS框架中引入动态调制矩阵，使系统能实时区分可通行的软区域与不可通行的硬区域，保障安全且灵活的轨迹规划。通过大量仿真与真实机器人实验验证，该方法具备在可变形环境中导航的能力。此外，该方法可在与可变形物体交互时（包括交叉点），同时控制轨迹与速度，保持原始DS轨迹不变，并动态适应障碍物，实现平滑可靠的导航。
原文摘要 · Abstract (English)
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.
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