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Slope Gradient Adaptive Gait Planning for Walking Assistance Lower Limb Exoskeletons

机译:坡度梯度自适应步态步行规划步行辅助下肢外骨骼

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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.
机译:近年来,下肢外骨骼对截瘫患者的行走援助施加相当兴趣。在日常生活中,外骨骼应该有能力帮助患者走遍不同的地形。对于倾斜的地形,如何规划具有不同梯度的斜坡上的步进位置,并为患者产生稳定的人类Gaits是一个关键问题。在本文中,我们提出了一种基于外骨骼的传感器数据融合的坡度梯度估计器(SGE),以及具有捕获点理论和动态运动原语(DMP)的组合SGE,以构建斜坡的自适应步态规划方法。在从健康受试者中抽样的演示Gaits学习之后,可以在线再现自适应步态轨迹,以适应不同梯度的斜坡。在名为Aider的外骨骼系统上证明了所提出的方法的效率。实验结果表明,所提出的方法可以赋予外骨骼,具有为不同斜坡产生适当的Gaits产生适当的Gaits。向从业者 - 对于下肢外骨骼来说,规划倾斜地形的步态是一个重要的问题。考虑到斜坡之间的不同渐变,固定的预定义步态规划无法涵盖所有​​情况;因此,需要斜率梯度自适应步态规划方法。本文提出的斜坡梯度估计器为外骨骼或人型双面机器人提供了可能的坡度梯度估计方法;仅基于机器人的本地传感器数据才能估计斜率梯度。所提出的动态步态发生器提供下肢外骨骼和人型双面机器人可以实现适应性的步态规划框架,以及不同斜坡的一些灵活性。拟议的方法可以激发更多的其他地形的长期规划战略,例如楼梯。

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