Environment-Adaptive Gait Planning for Obstacle Avoidance in Lower-Limb Robotic Exoskeletons

Abstract

Powered lower limb exoskeletons (LLEs) have emerged as wearable robots designed to augment users’ locomotion capabilities, offering mechanical support and additional power for both healthy and impaired subjects. However, current assistive exoskeletons are limited by predefined motion trajectories, hindering adaptability to unstructured environments encountered in daily life. To address this limitation, this paper proposes an environment-adaptive gait planning (EAGP) solution. The approach integrates scene understanding, pose estimation, and adaptive gait planning modules. A novel Collision-Free Foot Trajectory Generator (CFFTG) algorithm facilitates obstacle avoidance by computing collision-free foot trajectories, enhancing safety and adaptability. Through inverse kinematics, the planned trajectories are converted into angular joint trajectories for execution by low-level control. This comprehensive framework aims to enhance the adaptability and safety of LLEs, paving the way for broader real-world applications beyond clinical and research settings.