Human-in-the-loop layered architecture for control of a wearable ankle–foot robot

Uriel Martinez-Hernandez, Sina Firouzy, Pouyan Mehryar, Lin Meng, Craig Childs, Arjan Buis, Abbas A. Dehghani-Sanij

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
16 Downloads (Pure)

Abstract

Intelligent wearable robotics is a promising approach for the development of devices that can interact with people and assist them in daily activities. This work presents a novel human-in-the-loop layered architecture to control a wearable robot while interacting with the human body. The proposed control architecture is composed of high-, mid- and low-level computational and control layers, together with wearable sensors, for the control of a wearable ankle–foot robot. The high-level layer uses Bayesian formulation and a competing accumulator model to estimate the human posture during the gait cycle. The mid-level layer implements a Finite State Machine (FSM) to prepare the control parameters for the wearable robot based on the decisions from the high-level layer. The low-level layer is responsible for the precise control of the wearable robot over time using a cascade proportional–integral–derivative (PID) control approach. The human-in-the-loop layered architecture is systematically validated with the control of a 3D printed wearable ankle–foot robot to assist the human foot while walking. The assistance is applied lifting up the human foot when the toe-off event is detected in the walking cycle, and the assistance is removed allowing the human foot to move down and contact the ground when the heel-contact event is detected. Overall, the experiments in offline and real-time modes, undertaken for the validation process, show the potential of the human-in-the-loop layered architecture to develop intelligent wearable robots capable of making decisions and responding fast and accurately based on the interaction with the human body.

Original languageEnglish
Article number104353
Number of pages11
JournalRobotics and Autonomous Systems
Volume161
Early online date6 Jan 2023
DOIs
Publication statusPublished - 31 Mar 2023

Keywords

  • autonomous systems
  • Bayesian inference
  • layered architectures
  • sensorimotor control

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