用软生长机器人通过触觉感知实现未知环境的自主建图。
Linking Exteroception and Proprioception through Improved Contact Modeling for Soft Growing Robots
- 基于接触模型构建2D环境轨迹仿真器，融合外部感知与本体感知。
- 在均匀与非均匀环境中，采样策略快速逼近最优部署动作。
- 适合对柔性机器人探索、自主导航感兴趣的科研人员。
软生长机器人利用柔性变形可在少主动自由度下实现鲁棒驱动，尤其在非结构化环境中展现出导航潜力。若能更好理解其碰撞与形变行为，便可借助直接触觉测量推断环境结构。本文提出将软生长机器人作为测绘与探索工具：首先分析离散转向时的碰撞特性，进而建立基于几何的仿真模型以预测二维环境中的运动轨迹；最后通过蒙特卡洛采样，根据当前知识选择最优部署点。在均匀与非均匀环境中，该方法均能快速接近理想动作，验证了软生长机器人在复杂环境探索与建图中的可行性。
原文摘要 · Abstract (English)
Passive deformation due to compliance is a commonly used benefit of soft robots, providing opportunities to achieve robust actuation with few active degrees of freedom. Soft growing robots in particular have shown promise in navigation of unstructured environments due to their passive deformation. If their collisions and subsequent deformations can be better understood, soft robots could be used to understand the structure of the environment from direct tactile measurements. In this work, we propose the use of soft growing robots as mapping and exploration tools. We do this by first characterizing collision behavior during discrete turns, then leveraging this model to develop a geometry-based simulator that models robot trajectories in 2D environments. Finally, we demonstrate the model and simulator validity by mapping unknown environments using Monte Carlo sampling to estimate the optimal next deployment given current knowledge. Over both uniform and non-uniform environments, this selection method rapidly approaches ideal actions, showing the potential for soft growing robots in unstructured environment exploration and mapping.
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