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

Powered lower limb exoskeletons (LLEs) have emerged as wearable robots designed to augment users’ loco- motion capabilities, offering mechanical support and additional power for both healthy and impaired subjects. However, cur- rent assistive exoskeletons are limited by predefined motion trajectories, hindering adaptability to unstructured environ- ments 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 angu- lar 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.