Environment-Aware and Human-Cooperative Swing Control for Lower-Limb Prostheses in Diverse Obstacle Scenarios

TL;DR

This paper presents a novel environment-aware and human-cooperative control system for lower-limb prostheses that improves obstacle negotiation by integrating real-time obstacle detection with user intent, enhancing mobility in complex terrains.

Contribution

The study introduces a new control strategy combining environmental perception and user cooperation, enabling more natural and effective obstacle navigation for prosthetic limbs.

Findings

Achieved 100% success in over 180 obstacle negotiation trials.

Demonstrated effective obstacle clearance with varied obstacle heights and distances.

Validated system adaptability to diverse obstacle scenarios.

Abstract

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.