让机器人在不同地形上负重行走更稳定，提升抗干扰能力。
PANOS: Payload-Aware Navigation in Offroad Scenarios
- 融合本体感知与外部感知，弱监督实现自适应步态
- 无负载时稳定性提升44%，15磅负载下提升53%
- 适合复杂地形负重作业的机器人研发人员
自然界演化出人类在不同地形上行走的能力，依赖对物理特性的深入理解。类似地，腿式机器人需具备在复杂地形上携带多种任务负载稳定行走的能力。然而，传统地形适应方法在负载变化时易失效。本文提出PANOS，一种基于机载传感器的本体感知与外部感知融合的弱监督方法，使腿式机器人在各类地形上保持稳定步态。实验在多种地形和负载条件下验证了该方法的有效性：无负载时稳定性提升44%，15磅负载下提升53%；同时，在不同地形类型下，振动成本降低20%，优于现有最先进方法。
原文摘要 · Abstract (English)
Nature has evolved humans to walk on different terrains by developing a detailed understanding of their physical characteristics. Similarly, legged robots need to develop their capability to walk on complex terrains with a variety of task-dependent payloads to achieve their goals. However, conventional terrain adaptation methods are susceptible to failure with varying payloads. In this work, we introduce PANOS, a weakly supervised approach that integrates proprioception and exteroception from onboard sensing to achieve a stable gait while walking by a legged robot over various terrains. Our work also provides evidence of its adaptability over varying payloads. We evaluate our method on multiple terrains and payloads using a legged robot. PANOS improves the stability up to 44% without any payload and 53% with 15 lbs payload. We also notice a reduction in the vibration cost of 20% with the payload for various terrain types when compared to state-of-the-art methods.
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