A musculoskeletal model-based Assistance-As-Needed paradigm for assistive robotics

University of Technology, Sydney. Faculty of Engineering and Information Technology.Robotic systems which operate collaboratively with their human operators to provide assistance are becoming reality, and many different paradigms for administering this assistance have been developed. A promising paradigm is Assistance-As-Needed, which aims to provide physical assistance specific to the individual requirements of the operator. This requires that the needs of the operator be determined, which is challenging as they depend on both the task being performed, and the capability of the operator to perform it. Current solutions use performance-based methods which critique the operator from observations obtained during tasks, and then adapt assistance based on how they performed. This approach has shown success in applications such as robotic rehabilitation. However, empirical performance-based methods have inherent limitations, primarily due to the numerous observations required before the operator’s assistance needs can be determined. The ideal Assistance-As-Needed paradigm should be able to determine the operator’s assistance requirements without prior observations, and with respect to arbitrary tasks. This thesis presents a novel Assistance-As-Needed paradigm using models to estimate the assistance needs of the human operator. An optimisation model is developed which utilises a publicly available musculoskeletal model representing the human upper limb to estimate their strength, which is compared to the strength required by the task being performed to gauge their assistance requirements. An advantage of this model-based approach is it allows effects on the operator’s assistance requirements due to task and physiological factors to be predicted. Furthermore, it avoids many of the limitations faced by empirical performance-based approaches since it does not require empirical observations. The model-based paradigm is demonstrated and evaluated in a number of simulated tasks involving the upper limb. Calculated upper limb strength is analysed with respect to factors such as the limb position, the direction of force at the hand, and muscular impairment. The calculated strength is shown to predict behaviours similar to those described in the literature. Experimental evaluation is performed by implementing the paradigm on a specially developed robotic exoskeleton to govern the assistance it provides a subject in a number of experimental tasks. The model-based Assistance-As-Needed paradigm is shown to successfully govern assistance towards specific muscles when needed in the tasks performed. Means of improving the paradigm, including methods for fitting the model to the subject, and the inclusion of additional physiological factors in the calculation of their assistance requirements is discussed

Experimental evaluation of a model-based assistance-as-needed paradigm using an assistive robot

In robotic rehabilitation a promising paradigm is assistance-as-needed. This is because it promotes patient active participation which is essential for neuro-rehabilitation. A model-based assistance-as-needed paradigm has been developed which utilizes a musculoskeletal model representing the subject to calculate their assistance needs. In this paper we experimentally evaluate this model-based paradigm to control an assistive robot and provide a subject with assistance-as-needed at the muscular level. A subject with impairments defined in specific muscle groups performs a number of upper limb tasks, whilst receiving assistance from a robotic exoskeleton. The paradigm is evaluated on its ability to provide assistance only as the subject needs, depending on the tasks being performed and the impairments defined. Results show that the model-based assistance-as-needed paradigm was relatively successful in providing assistance when it was needed. © 2013 IEEE

Upper limb strength estimation of physically impaired persons using a musculoskeletal model: A sensitivity analysis

© 2015 IEEE. Sensitivity of upper limb strength calculated from a musculoskeletal model was analyzed, with focus on how the sensitivity is affected when the model is adapted to represent a person with physical impairment. Sensitivity was calculated with respect to four muscle-tendon parameters: muscle peak isometric force, muscle optimal length, muscle pennation, and tendon slack length. Results obtained from a musculoskeletal model of average strength showed highest sensitivity to tendon slack length, followed by muscle optimal length and peak isometric force, which is consistent with existing studies. Muscle pennation angle was relatively insensitive. The analysis was repeated after adapting the musculoskeletal model to represent persons with varying severities of physical impairment. Results showed that utilizing the weakened model significantly increased the sensitivity of the calculated strength at the hand, with parameters previously insensitive becoming highly sensitive. This increased sensitivity presents a significant challenge in applications utilizing musculoskeletal models to represent impaired individuals

Human biomechanical model based optimal design of assistive shoulder exoskeleton

© Springer International Publishing Switzerland 2015. Robotic exoskeletons are being developed to assist humans in tasks such as robotic rehabilitation, assistive living, industrial and other service applications. Exoskeletons for the upper limb are required to encompass the shoulder whilst achieving a range of motion so as to not impede the wearer, avoid collisions with the wearer, and avoid kinematic singularities during operation. However this is particularly challenging due to the large range of motion of the human shoulder. In this paper a biomechanical model based optimisation is applied to the design of a shoulder exoskeleton with the objective of maximising shoulder range of motion. A biomechanical model defines the healthy range of motion of the human shoulder. A genetic algorithm maximises the range of motion of the exoskeleton towards that of the human, whilst taking into account collisions and kinematic singularities. It is shown how the optimisation can increase the exoskeleton range of motion towards that equivalent of the human, or towards a subset of human range of motion relevant to specific applications

Admittance control scheme for implementing model-based assistance-as-needed on a robot

A model-based assistance-as-needed paradigm has been developed to govern the assistance provided by an assistive robot to its operator. This paradigm has advantages over existing methods of providing assistance-as-needed for applications such as robotic rehabilitation. However, implementation of the model-based paradigm requires a control scheme to be developed which controls the robot so as to provide the assistance calculated by the model-based paradigm to its operator. In this paper an admittance control scheme for providing model-based assistance-as-needed is presented. It is developed considering its suitability for human-robot interaction, and its role within the model-based assistance-as-needed framework. Results from the control implemented on an example robot showed it is capable of providing the operator with the desired level of assistance as governed by the model-based paradigm. This is an essential requirement for the paradigm to be capable of providing efficacious assistance-as-needed in applications such as robotic rehabilitation. © 2013 IEEE

Towards using musculoskeletal models for intelligent control of physically assistive robots

With the increasing number of robots being developed to physically assist humans in tasks such as rehabilitation and assistive living, more intelligent and personalized control systems are desired. In this paper we propose the use of a musculoskeletal model to estimate the strength of the user, from which information can be utilized to improve control schemes in which robots physically assist humans. An optimization model is developed utilizing a musculoskeletal model to estimate human strength in a specified dynamic state. Results of this optimization as well as methods of using it to observe muscle-based weaknesses in task space are presented. Lastly potential methods and problems in incorporating this model into a robot control system are discussed. © 2011 IEEE

Investigation of reducing fatigue and musculoskeletal disorder with passive actuators

Robotic systems such as exoskeletons can be effectively used in the reduction of fatigue and musculoskeletal disorders (MSD) associated with physical tasks, but robots which work in physical contact with humans pose problems with user safety. A novel approach to developing intrinsically safe robots is to use passive actuators which have the advantage of being safer, ensuring stability, high force/weight ratios and lower power consumption. It is however not clear how effective an exoskeleton utilizing passive actuators would be in reducing fatigue and the risk of MSD. This paper analyzes the benefit of using such a system with results from dynamic simulations and an experiment using a specially designed mechanism used for evaluation. Results indicate that fatigue and effort could be reduced if robot impedance is minimized. Experiments also highlighted issues of implementing such a system into practice. ©2010 IEEE

A framework for task-based evaluation of robotic coworkers

© 2014 IEEE. Compared to a robotic system that performs a task alone, a robot coworker performing tasks in collaboration with a human operator is subject to additional constraints which can limit the ability of the system to perform the task as required. This work presents a framework for analyzing the ability of a robotic coworker to perform specific tasks in collaboration with a human. The framework allows systematic evaluation of robotic systems based on traditional robot performance measures such as reachable workspace and payload capacity, as well as considering additional factors which arise due to the task being performed collaboratively with a human; such as the reach and strength of the human, human-robot collision, and satisfying desired assistance paradigms. Application of the framework is demonstrated in a case study analyzing a robot designed to assist a human during a materials handling task

Human User Impressions of Damping Methods for Singularity Handling in Human-Robot Collaboration

Kinematic singularity is a fundamental and well understood problem of robot manipulators, with many methods having been developed to ensure safe and robust operation in proximity to singularity. However little attention has been given to the scenario where the robot and human are working in physical contact to collaboratively perform a task. In such a scenario the feelings and impressions of the human operator should be considered when developing solutions for handling singularity. This work presents an experimental study comparing three modes of handling kinematic singularities with respect to the impressions of the human operator. Two of the modes are based on traditional Damped-Least-Squares. The third method uses an asymmetric damping behavior proposed as being well suited for applications involving physical human-robot interaction. The three modes are tested and compared by subjects performing a mock industrial task, and feedback from the subjects analyzed to identify the preferred mode. Results indicate that the choice of method used affects the user’s impressions of the interaction, and the asymmetrical damping behavior can produce a preferred interaction experience with human operators during tasks

A Risk Reduction Framework for Design of Physical Human-Robot Collaboration

As robots designed to physically interact with humans become common in various application areas, shared workspaces and force exchange between human and robot lead to new challenges in terms of safety. Often, a variety of safety techniques is necessary, and deciding what methods to include in a comprehensive safety framework is not an easy task. This paper is concerned with the design of robotic co-wokers that involve physical Human-Robot Collaboration (pHRC), with humans and robots in continuous direct physical contact and exchanging forces. A hierarchical risk reduction framework is presented for guiding the design of robotic co-workers to reduce the risk associated with hazards commonly found in pHRC tasks. A case study is presented to demonstrate the use of the framework in designing an Assistance-as-Needed roBOT (ANBOT) which has been extensively tested in practical industry applications