[论文解读] Environmental force sensing helps robots traverse cluttered large obstacles using physical interaction

Qihan Xuan, Chen Li|arXiv (Cornell University)|Dec 15, 2021

Robotic Locomotion and Control被引用 4

一句话总结

本文提出了一种基于力反馈的控制策略，使机器人能够通过环境力感知与物理交互，穿越杂乱且体积较大的障碍物。通过物理模型从感知到的力中估计梁的刚度，机器人可在不同运动模式（如俯仰与滚动）间切换，从而降低能耗并提高穿越成功率，在模拟实验中优于固定推挤或避让策略。

ABSTRACT

Many applications require robots to move through complex 3-D terrain with large obstacles, such as self-driving, search and rescue, and extraterrestrial exploration. Although robots are already excellent at avoiding sparse obstacles, they still struggle in traversing cluttered large obstacles. To make progress, we need to better understand how to use and control the physical interaction with obstacles to traverse them. Forest floor-dwelling cockroaches can use physical interaction to transition between different locomotor modes to traverse flexible, grass-like beams of a large range of stiffness. Inspired by this, here we studied whether and how environmental force sensing helps robots make active adjustments to traverse cluttered large obstacles. We developed a physics model and a simulation of a minimalistic robot capable of sensing environmental forces during traversal of beam obstacles. Then, we developed a force-feedback control strategy, which estimated beam stiffness from the sensed contact force using the physics model. Then in simulation we used the estimated stiffness to control the robot to either stay in or transition to the more favorable locomotor modes to traverse. When beams were stiff, force sensing induced the robot to transition from a more costly pitch mode to a less costly roll mode, which helped the robot traverse with a higher success rate and less energy consumed. By contrast, if the robot simply pushed forward or always avoided obstacles, it would consume more energy, become stuck in front of beams, or even flip over. When the beams were flimsy, force sensing guided the robot to simply push across the beams. In addition, we demonstrated the robustness of beam stiffness estimation against body oscillations, randomness in oscillation, and uncertainty in position sensing. We also found that a shorter sensorimotor delay reduced energy cost of traversal.

研究动机与目标

• 理解机器人如何利用与大型柔性障碍物的物理交互来改善在复杂三维地形中的穿越能力。
• 探究环境力感知是否能够在障碍物穿越过程中实现主动适应。
• 开发一种利用力反馈估计障碍物刚度并选择最优运动模式的控制策略。
• 评估在身体振荡、传感器噪声和位置不确定性条件下刚度估计的鲁棒性。
• 量化传感器运动延迟对穿越过程中能效的影响。

提出的方法

• 开发了一种极简化的机器人模型，通过物理仿真模拟其与梁状障碍物的相互作用。
• 使用物理模型将接触力与梁的刚度关联起来，以描述穿越过程中的力学行为。
• 利用力反馈实时估计接触力所对应的梁刚度。
• 实现了一种控制策略，根据估计的刚度在不同运动模式（如俯仰与滚动）之间切换。
• 在多种条件下对系统进行了测试，包括身体振荡、随机扰动以及位置感知不确定性。
• 调节传感器运动延迟以评估其对能耗与穿越性能的影响。

实验结果

研究问题

• RQ1 环境力感知是否能够使机器人在遇到大型柔性障碍物时主动适应其运动模式？
• RQ2 从接触力中估计刚度如何提升穿越成功率与能效？
• RQ3 传感器运动延迟对障碍物穿越能耗有何影响？
• RQ4 刚度估计方法在身体振荡与传感器噪声下的鲁棒性如何？
• RQ5 基于刚度估计在运动模式间切换是否相比固定策略能降低能耗？

主要发现

• 当梁较刚性时，力感知使机器人能够从高能耗的俯仰模式切换至更高效的滚动模式，显著提高穿越成功率并降低能耗。
• 对于柔弱的梁，力感知使机器人可直接推过障碍物，无需复杂模式切换，同时保持高成功率与低能耗。
• 刚度估计方法在身体振荡、随机振荡模式及位置感知不确定性下仍保持鲁棒性。
• 较短的传感器运动延迟可降低穿越过程中的能耗，凸显及时反馈的重要性。
• 固定策略（如始终向前推挤或始终避让障碍物）在刚性梁上导致更高能耗、卡滞或翻倒，表现更差。