低成本轮足机器人实现视觉导航与跌倒自恢复。
Stand, Walk, Navigate: Recovery-Aware Visual Navigation on a Low-Cost Wheeled Quadruped
- 用深度相机+强化学习实现鲁棒行走与自恢复。
- 低扭矩电机下在不平地形仍能敏捷移动并抗扰动。
- 适合预算有限的机器人平台快速部署自主导航。
轮足机器人结合了轮子的高效性与腿部的越障能力，但许多先进系统依赖昂贵的执行器和传感器，且极少集成跌倒恢复功能，尤其针对轮足形态。本文提出一种面向低成本轮足机器人的恢复感知视觉-惯性导航系统。该系统利用深度相机进行视觉感知，并采用深度强化学习策略，实现复杂地形下的稳健运动与自主跌倒恢复。仿真结果表明，在低扭矩执行器条件下，机器人可在不规则地形上实现敏捷移动，并可靠应对外部扰动与自诱发故障。此外，系统还实现了结构化室内环境中的目标导向导航，仅需低成本感知设备。整体方法显著降低了在资源受限机器人平台上部署自主导航与鲁棒运动策略的门槛。
原文摘要 · Abstract (English)
Wheeled-legged robots combine the efficiency of wheels with the obstacle negotiation of legs, yet many state-of-the-art systems rely on costly actuators and sensors, and fall-recovery is seldom integrated, especially for wheeled-legged morphologies. This work presents a recovery-aware visual-inertial navigation system on a low-cost wheeled quadruped. The proposed system leverages vision-based perception from a depth camera and deep reinforcement learning policies for robust locomotion and autonomous recovery from falls across diverse terrains. Simulation experiments show agile mobility with low-torque actuators over irregular terrain and reliably recover from external perturbations and self-induced failures. We further show goal directed navigation in structured indoor spaces with low-cost perception. Overall, this approach lowers the barrier to deploying autonomous navigation and robust locomotion policies in budget-constrained robotic platforms.
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