让假肢智能识别障碍物并配合用户意图，轻松跨过高低不一的障碍。
Environment-Aware and Human-Cooperative Swing Control for Lower-Limb Prostheses in Diverse Obstacle Scenarios
- 用前置深度摄像头感知障碍物，提前抬高摆动腿轨迹以避障。
- 实验中150多次跨步和30多次踩踏障碍物均成功，成功率100%。
- 适合需要复杂地形行走的假肢用户，提升自然交互体验。
当前动力下肢假肢的控制策略常缺乏环境感知能力及对用户意图的响应。在复杂地形中，跨越障碍是典型挑战，需实时感知障碍几何形状，并根据用户意图决定何时何地跨过或踏上。本文提出一种融合环境感知与人机协作的新控制策略：在摆动前期，由机载深度相机检测前方障碍物，触发抬高早期摆动轨迹以确保通过；在摆动后期，则优先响应用户的自然生物力学信号。该方法使用户无需刻意调整动作即可实现自然步态。三名非截肢者参与实验，在随机放置、高度4-16厘米、距离15-70厘米的多种障碍物条件下，完成超过150次跨步和30次踩踏任务，成功率100%。该系统有效应对了复杂地形通行的关键挑战，兼具环境适应性与用户意图响应能力，具备广泛的应用前景。
原文摘要 · Abstract (English)
Current control strategies for powered lower limb prostheses often lack awareness of the environment and the user's intended interactions with it. This limitation becomes particularly apparent in complex terrains. Obstacle negotiation, a critical scenario exemplifying such challenges, requires both real-time perception of obstacle geometry and responsiveness to user intention about when and where to step over or onto, to dynamically adjust swing trajectories. We propose a novel control strategy that fuses environmental awareness and human cooperativeness: an on-board depth camera detects obstacles ahead of swing phase, prompting an elevated early-swing trajectory to ensure clearance, while late-swing control defers to natural biomechanical cues from the user. This approach enables intuitive stepping strategies without requiring unnatural movement patterns. Experiments with three non-amputee participants demonstrated 100 percent success across more than 150 step-overs and 30 step-ons with randomly placed obstacles of varying heights (4-16 cm) and distances (15-70 cm). By effectively addressing obstacle navigation -- a gateway challenge for complex terrain mobility -- our system demonstrates adaptability to both environmental constraints and user intentions, with promising applications across diverse locomotion scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。