Probabilistic movement primitive based motion learning for a lower limb exoskeleton with black-box optimization

As a wearable robot,an exoskeleton provides a direct transfer of mechanical power to assist or augment the wearer’s movement with an anthropomorphic configuration.When an exoskeleton is used to facilitate the wearer’s movement,a motion generation process often plays an important role in high-level control.One of the main challenges in this area is to generate in real time a reference trajectory that is parallel with human intention and can adapt to different situations.In this paper,we first describe a novel motion modeling method based on probabilistic movement primitive(ProMP)for a lower limb exoskeleton,which is a new and powerful representative tool for generating motion trajectories.To adapt the trajectory to different situations when the exoskeleton is used by different wearers,we propose a novel motion learning scheme based on black-box optimization(BBO)PIBB combined with ProMP.The motion model is first learned by ProMP offline,which can generate reference trajectories for use by exoskeleton controllers online.PIBB is adopted to learn and update the model for online trajectory generation,which provides the capability of adaptation of the system and eliminates the effects of uncertainties.Simulations and experiments involving six subjects using the lower limb exoskeleton HEXO demonstrate the effectiveness of the proposed methods.

Integral Real-time Locomotion Mode Recognition Based on GA-CNN for Lower Limb Exoskeleton

The wearable lower limb exoskeleton is a typical human-in-loop human–robot coupled system,which conducts natural and close cooperation with the human by recognizing human locomotion timely.Requiring subject-specific training is the main challenge of the existing approaches,and most methods have the problem of insufficient recognition.This paper proposes an integral subject-adaptive real-time Locomotion Mode Recognition(LMR)method based on GA-CNN for a lower limb exoskeleton system.The LMR method is a combination of Convolutional Neural Networks(CNN)and Genetic Algorithm(GA)-based multi-sensor information selection.To improve network performance,the hyper-parameters are optimized by Bayesian optimization.An exoskeleton prototype system with multi-type sensors and novel sensing-shoes is used to verify the proposed method.Twelve locomotion modes,which composed an integral locomotion system for the daily application of the exoskeleton,can be recognized by the proposed method.According to a series of experiments,the recognizer shows strong comprehensive abilities including high accuracy,low delay,and sufficient adaption to different subjects.