In recent years, lower limb exoskeletons have gained considerable interest in applications of walking assistance for paraplegic patients. In daily lives, the exoskeleton should have the ability to help the patients to walk over different terrains. For sloped terrains, how to plan the stepping locations on slopes with different gradients and generate stable human-like gaits for patients is a critical issue. In this article, we proposed a slope gradient estimator (SGE) based on the sensor data fusion of the exoskeleton and combined SGE with the capture point theory and dynamic movement primitives (DMP) to construct an adaptive gait planning approach for slopes. After learning from demonstrated gaits sampled from healthy subjects, adaptive gait trajectories can be reproduced online to adapt to slopes with different gradients. The efficiency of the proposed approach was demonstrated on an exoskeleton system named AIDER. Experimental results indicate that the proposed approach can endow exoskeletons with the ability to generate appropriate gaits for different slopes. Note to Practitioners-For lower limb exoskeletons, it is a vital problem to plan the gait for sloped terrains. Considering different gradients among slopes, fixed predefined gait planning cannot cover all cases; thus, a slope gradient adaptive gait planning approach is necessary. The slope gradient estimator proposed in this article provides a possible slope gradient estimation method for exoskeletons or humanoid bipedal robots; it is easy to estimate the slope gradient only based on the local sensor data of the robot. The proposed dynamic gait generator provides lower limb exoskeletons and humanoid bipedal robots a possible adaptive gait planning framework and some flexibility for different slopes. The proposed approach may inspire more extended gait planning strategies for other terrains, such as stairs.

中文翻译：

---

用于步行辅助下肢外骨骼的坡度梯度自适应步态规划


近年来，下肢外骨骼在截瘫患者行走辅助方面的应用引起了人们的极大兴趣。在日常生活中，外骨骼应该能够帮助患者在不同的地形上行走。对于倾斜地形，如何规划不同坡度的斜坡上的踏步位置，并为患者生成稳定的类人步态是一个关键问题。在本文中，我们提出了一种基于外骨骼传感器数据融合的坡度梯度估计器（SGE），并将SGE与捕获点理论和动态运动基元（DMP）相结合，构建了坡度自适应步态规划方法。从健康受试者的示范步态中学习后，可以在线复制自适应步态轨迹，以适应不同坡度的坡度。该方法的有效性在名为 AIDER 的外骨骼系统上得到了证明。实验结果表明，所提出的方法可以赋予外骨骼针对不同坡度生成适当步态的能力。从业者须知——对于下肢外骨骼来说，规划倾斜地形的步态是一个至关重要的问题。考虑到坡度不同，固定的预定义步态规划无法覆盖所有情况；因此，需要一种坡度梯度自适应步态规划方法。本文提出的坡度梯度估计器为外骨骼或类人双足机器人提供了一种可能的坡度梯度估计方法；仅根据机器人本地传感器数据即可轻松估计坡度梯度。所提出的动态步态生成器为下肢外骨骼和人形双足机器人提供了可能的自适应步态规划框架以及针对不同坡度的一定灵活性。 所提出的方法可能会激发针对其他地形（例如楼梯）的更广泛的步态规划策略。