The relationship between physical human-exoskeleton interaction and dynamic factors: using a learning approach for control applications

During a human-exoskeleton collaboration, the interaction torque on exoskeleton resulting from the human cannot be clearly determined and conducted by normal physical models. This is because the torque depends not only on direction and orientation of both human-operator and exoskeleton but also on the physical properties of each operator. In this paper, we present our investigations on the relationship between the interaction torques with the dynamic factors of the human-exoskeleton systems using state-of-the-art learning techniques (nonparametric regression techniques) and provide control applications based on the findings. Experimental data was collected from various human-operators when they were attached to the designed exoskeleton to perform unconstraint motions with and without control. The results showed that regardless of how the experiments were done and which learning method was chosen, the resulting interaction could be best represented by time varying non-linear mappings of the operator’s angular position, and the exoskeleton’s angular position, velocity, and acceleration during locomotion. This finding has been applied to advanced controls of the lower exoskeletal robots in order to improve their performance while interacting with human.概要创新点在穿戴者与外骨骼的人机交互协作过程中, 人体作用于外骨骼的交互力矩不仅与人机运动方向有关, 同时也取决于穿戴者自身的行走或运动习惯. 因此, 人机交互力矩的模型很难通过常规的物理模型来确定. 本文通过对机器学习技术(非参数化回归)最新发展情况的介绍来探究人与外骨骼交互过程中交互力矩与系统动力学因素之间的关系, 并在其结果基础上提出相关的控制应用. 我们邀请了不同的穿戴者进行测试实验, 每个穿戴者的测试都分别在有控制和无控制两种情况下进行了无约束的行走实验, 在这些测试基础上我们采集了我们需要的实验数据. 通过对实验数据的学习我们发现, 交互结果能够通过将穿戴者行走过程中的角度位置与在此过程中外骨骼的角度位置、 速度和加速度进行时变非线性映射来表征. 实验结果已经被用于外骨骼机器人的高级控制算法, 改善和提高了外骨骼与穿戴者交互性的表现.