论文标题
步态图优化：从一个基本步态生成可变步态，以进行下限恢复外骨骼机器人机器人
Gait Graph Optimization: Generate Variable Gaits from One Base Gait for Lower-limb Rehabilitation Exoskeleton Robots
论文作者
论文摘要
下LIMB康复外骨骼（LLE）机器人最集中的应用是它可以帮助截瘫的“重新步行”。但是，日常生活中的“步行”不仅仅是在固定步态的平坦地面上行走。本文着重于LLE机器人适应复杂步行环境的可变步态生成。不同于传统步态生成器，用于双头机器人，LLE的生成步态应该对患者舒适。受SLAM中的姿势图优化算法的启发，我们提出了一种基于图的步态生成算法，称为步态图优化（GGO），以从健康个体收集的一个基本步态中生成可变，功能和舒适的步态，以适应步行环境。步行问题的变体，例如，步伐调整，避免障碍物以及楼梯上升和下降，有助于验证拟议的模拟和实验方法。我们开源的实施。
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.
