Duygun Erol

Coordinated Control of Assistive Robotic Devices for Activities of Daily Living Tasks

Recent research in rehabilitation indicates that tasks that focus on activities of daily living (ADL) are likely to show significant increase in motor recovery after stroke. Most ADL tasks require patients to coordinate their arm and hand movements to complete these tasks. This paper presents a new control approach for robot-assisted rehabilitation of stroke patients that enables them to perform ADL by providing controlled and coordinated assistance to both arm and hand movement. The control architecture is represented in terms of a hybrid system model combining a high-level controller for decision-making and two low-level assistive controllers (arm and hand controllers) for arm and hand motion assistance. The presented controller is implemented on a test-bed and the results of this implementation are presented to demonstrate the feasibility of the proposed control architecture.

Design and Implementation of an Assistive Controller for Rehabilitation Robotic Systems

The goal of our research is to develop an assistive controller for robotic rehabilitation of the upper extremity after stroke. The controller is used to provide robotic assistance to participants to help them to track a desired motion trajectory required for the rehabilitation task in an accurate and concentrated manner. This rehabilitation task is designed to ensure concentrated repetitive motion that requires cognitive processing. Experimental results on unimpaired participants are presented to demonstrate the effectiveness and feasibility of the proposed controller.

Intelligent Control for Robotic Rehabilitation after Stroke

The paper presents a new control approach to robot-assisted rehabilitation for stroke patients. The control architecture is represented in terms of a hybrid system model combining a high-level and a low-level assistive controller. The high-level controller is designed to monitor the progress and safety of the rehabilitation task. The high-level controller also makes decisions on the modification of the task that might be needed for the therapy. A design of a low-level assistive controller that provides robotic assistance for an upper arm rehabilitation task and works in coordination with the proposed high-level controller is discussed. Experimental results on unimpaired participants are presented to demonstrate the efficacy of both the high-level and low-level assistive controllers.

Motion generation for humanoid robots with automatically derived behaviors

In this paper, we present a method for motion generation from automatically derived behaviors for a humanoid robot. Behaviors are derived automatically by using the underlying spatio-temporal structure in motion. The derived behaviors are stored in a robots long-term (or procedural) memory. New motions are generated from the derived ones with a search mechanism. In our approach, vision, speech recognition, short-term memory and decision-making operate in parallel with long-term memory in a unique architecture. This organization is intended for autonomous robot control and learning.

A new control approach to robot assisted rehabilitation

The goal of our research is to develop a novel control framework to assist stroke patients during rehabilitation therapy. This framework is expected to provide an optimal time-varying assistive force to stroke patients in varying physical and environmental conditions. An artificial neural network (ANN)-based PI-gain scheduling direct force controller is designed to provide optimal force assistance. The human arm model is integrated within the control framework where the ANN uses estimated human arm parameters to select the appropriate PI gains. An online technique to estimate human arm parameters as well as off-line analyses of the force controller are presented in this paper to demonstrate the feasibility and efficacy of the proposed method.

A New Method of Force Control for Unknown Environments

We propose a new control technique for force control on unknown environments. In particular, the proposed approach overcomes the need for precise estimation of environment parameters, which are needed in many system identification-based force control approaches. This framework uses an artificial neural network (ANN)-based proportional-integral (PI)-gain scheduling direct force controller to track the desired force by adjusting control gains based on online parameter estimation. However, the ANN is tolerant to imprecise estimation of environment parameters. Experimental results are presented to demonstrate the efficacy of the proposed control framework. Finally, the advantages and limitations of the proposed controller are discussed

Autonomously Adapting Robotic Assistance for Rehabilitation Therapy

The goal of our research is to develop a novel control framework to provide robotic assistance for rehabilitation of the hemiparetic upper extremity after stroke. The control framework is designed to provide an optimal time-varying assistive force to stroke patients in varying physical and environmental conditions. An artificial neural network (ANN) based proportional-integral (PI) gain scheduling direct force controller is designed to provide optimal force assistance in a precise and smooth manner. The human arm model is integrated within the control framework where ANN uses estimated human arm parameters to select the appropriate PI gains. Experimental results are presented to demonstrate the effectiveness and feasibility of the proposed control framework

Intelligent Control Framework for Robotic Rehabilitation after Stroke

This paper presents a new approach to robot assisted rehabilitation for stroke patients. The control architecture is represented in terms of hybrid system model combining a high-level and a low-level controller. The main focus of this paper is to present an intelligent controller, which is the high-level controller in the control architecture. The high-level controller is designed to monitor the progress and safety of the rehabilitation task. It also makes decisions on the modification of the task that might be needed for the therapy. Experimental results on unimpaired subjects are presented to demonstrate the efficacy of the high-level controller.

Towards a human–robot symbiotic system

Abstract Partnership between a person and a robot could be simplified if the robot were intelligent enough to understand human intentions and perform accordingly. During the last decade, we have been developing such an intelligent robot called ISAC. Originally, ISAC was designed to assist the physically disabled, but gradually became a test bed for more robust human–robot teaming (see http://eecs.vanderbilt.edu/CIS/ ). In this paper, we will describe a framework for human–robot interaction, a multi-agent based robot control architecture, and short- and long-term memory structures for the robot brain. Two applications will illustrate how ISAC interacts with the human.

Smooth Human-Robot Interaction in Robot-Assisted Rehabilitation

The goal of this work is to develop a control framework to provide robotic assistance for rehabilitation tasks to the subjects in such a manner that the interaction between the subject and the robot is smooth. This is achieved by designing a methodology that automatically adjusts the control gains of the robot controller to modify the interaction dynamics between the robot and the subject. In order to automatically determine the control gains for each subject, an artificial neural network (ANN) based proportional-integral (PI) gain scheduling controller is proposed. The human arm model is integrated within the controller where the ANN uses estimated human arm parameters to select the appropriate PI gains for each subject such that the resultant interaction dynamics between the subject and the robot could result in smooth interaction. Experimental results are presented to demonstrate the efficacy of the proposed ANN-based PI gain scheduling controller on unimpaired subjects.