Gait Graph Optimization: Generate Variable Gaits from One Base Gait for Lower-limb Rehabilitation Exoskeleton Robots
This work addresses the need for adaptable and comfortable gait generation for paraplegics using exoskeleton robots, representing an incremental improvement by applying graph optimization from SLAM to a new domain.
The paper tackles the problem of generating variable gaits for lower-limb rehabilitation exoskeleton robots to adapt to complex walking environments, proposing a graph-based algorithm that generates comfortable gaits from a base gait, with verification through simulations and experiments for tasks like stride adjustment and stair ascent.
The most concentrated application of lower-limb rehabilitation exoskeleton (LLE) robot is that it can help paraplegics "re-walk". However, "walking" in daily life is more than just walking on flat ground with fixed gait. This paper focuses on variable gaits generation for LLE robot to adapt complex walking environment. Different from traditional gaits generator for biped robot, the generated gaits for LLEs should be comfortable to patients. Inspired by the pose graph optimization algorithm in SLAM, we propose a graph-based gait generation algorithm called gait graph optimization (GGO) to generate variable, functional and comfortable gaits from one base gait collected from healthy individuals to adapt the walking environment. Variants of walking problem, e.g., stride adjustment, obstacle avoidance, and stair ascent and descent, help verify the proposed approach in simulation and experimentation. We open source our implementation.