Variable stiffness control for oscillation damping

In this paper a model-free approach for damping control of Variable Stiffness Actuators is proposed. The idea is to take advantage of the possibility to change the stiffness of the actuators in controlling the damping. The problem of minimizing the terminal energy for a one degree of freedom spring-mass model with controlled stiffness is first considered. The optimal bang-bang control law uses a maximum stiffness when the link gets away from the desired position, i.e. the link velocity is decreasing, and a minimum one when the link is going towards it, i.e. the link velocity is increasing. Based on Lyapunov stability theorems the obtained law has been proved to be stable for a multi-DoF system. Finally, the proposed control law has been tested and validated through experimental tests

Design and development of the ‘POD Adventures’ smartphone game: a blended problem-solving intervention for adolescent mental health in India

Introduction: Digital technology platforms offer unparalleled opportunities to reach vulnerable adolescents at scale and overcome many barriers that exist around conventional service provision. This paper describes the design and development of POD Adventures, a blended problem-solving game-based intervention for adolescents with or at risk of anxiety, depression and conduct difficulties in India. This intervention was developed as part of the PRemIum for ADolEscents (PRIDE) research programme, which aims to establish a suite of transdiagnostic psychological interventions organised around a stepped care system in Indian secondary schools. Methods and materials: Intervention development followed a person-centered approach consisting of four iterative activities: (i) review of recent context-specific evidence on mental health needs and preferences for the target population of school-going Indian adolescents, including a multiple stakeholder analysis of school counselling priorities and pilot studies of a brief problem-solving intervention; (ii) new focus group discussions with N=46 student participants and N=8 service providers; (iii) co-design workshops with N=22 student participants and N=8 service providers; and (iv) user-testing with N=50 student participants. Participants were aged 12-17 years and recruited from local schools in New Delhi and Goa, including a subgroup with self-identified mental health needs (N=6). Results: Formative data from existing primary sources, new focus groups and co-design workshops supported a blended format for delivering a brief problem-solving intervention, with counsellors supporting use of a game-based app on ‘offline’ smartphones. User-testing with prototypes identified a need for simplification of language, use of concrete examples of concepts and practice elements to enhance engagement. There were also indications that participants most valued relatability and interactivity within real-world stories with judicious support from an in-app guide. The final prototype comprised a set of interactive and gamified vignettes and a structured set of problem-solving questions to consolidate and generalise learning while encouraging real-world application. Discussion: Findings shaped the design of POD Adventures and its delivery as an open-access blended intervention for secondary school students with a felt need for psychological support, consistent with an early intervention paradigm. A randomised controlled trial is planned to evaluate processes and impacts of POD Adventures when delivered for help-seeking students in low-resource school settings

Design and development of the ‘POD Adventures’ smartphone game: a blended problem-solving intervention for adolescent mental health in India

Introduction: Digital technology platforms offer unparalleled opportunities to reach vulnerable adolescents at scale and overcome many barriers that exist around conventional service provision. This paper describes the design and development of POD Adventures, a blended problem-solving game-based intervention for adolescents with or at risk of anxiety, depression and conduct difficulties in India. This intervention was developed as part of the PRemIum for ADolEscents (PRIDE) research programme, which aims to establish a suite of transdiagnostic psychological interventions organised around a stepped care system in Indian secondary schools. Methods and materials: Intervention development followed a person-centered approach consisting of four iterative activities: (i) review of recent context-specific evidence on mental health needs and preferences for the target population of school-going Indian adolescents, including a multiple stakeholder analysis of school counselling priorities and pilot studies of a brief problem-solving intervention; (ii) new focus group discussions with N=46 student participants and N=8 service providers; (iii) co-design workshops with N=22 student participants and N=8 service providers; and (iv) user-testing with N=50 student participants. Participants were aged 12-17 years and recruited from local schools in New Delhi and Goa, including a subgroup with self-identified mental health needs (N=6). Results: Formative data from existing primary sources, new focus groups and co-design workshops supported a blended format for delivering a brief problem-solving intervention, with counsellors supporting use of a game-based app on ‘offline’ smartphones. User-testing with prototypes identified a need for simplification of language, use of concrete examples of concepts and practice elements to enhance engagement. There were also indications that participants most valued relatability and interactivity within real-world stories with judicious support from an in-app guide. The final prototype comprised a set of interactive and gamified vignettes and a structured set of problem-solving questions to consolidate and generalise learning while encouraging real-world application. Discussion: Findings shaped the design of POD Adventures and its delivery as an open-access blended intervention for secondary school students with a felt need for psychological support, consistent with an early intervention paradigm. A randomised controlled trial is planned to evaluate processes and impacts of POD Adventures when delivered for help-seeking students in low-resource school settings

Assessment of Myoelectric Controller Performance and Kinematic Behavior of a Novel Soft Synergy-Inspired Robotic Hand for Prosthetic Applications

Myoelectric artificial limbs can significantly advance the state of the art in prosthetics, since they can be used to control mechatronic devices through muscular activity in a way that mimics how the subjects used to activate their muscles before limb loss. However, surveys indicate that dissatisfaction with the functionality of terminal devices underlies the widespread abandonment of prostheses. We believe that one key factor to improve acceptability of prosthetic devices is to attain human likeness of prosthesis movements, a goal which is being pursued by research on social and human-robot interactions. Therefore, to reduce early abandonment of terminal devices, we propose that controllers should be designed so as to ensure effective task accomplishment in a natural fashion. In this work, we have analyzed and compared the performance of three types of myoelectric controller algorithms based on surface electromyography to control an underactuated and multi-degrees of freedom prosthetic hand, the SoftHand Pro. The goal of the present study was to identify the myoelectric algorithm that best mimics the native hand movements. As a preliminary step, we first quantified the repeatability of the SoftHand Pro finger movements and identified the electromyographic recording sites for able-bodied individuals with the highest signal-to-noise ratio from two pairs of muscles, i.e., flexor digitorum superficialis/extensor digitorum communis, and flexor carpi radialis/extensor carpi ulnaris. Able-bodied volunteers were then asked to execute reach-to-grasp movements, while electromyography signals were recorded from flexor digitorum superficialis/extensor digitorum communis as this was identified as the muscle pair characterized by high signal-to-noise ratio and intuitive control. Subsequently, we tested three myoelectric controllers that mapped electromyography signals to position of the SoftHand Pro. We found that a differential electromyography-to-position mapping ensured the highest coherence with hand movements. Our results represent a first step toward a more effective and intuitive control of myoelectric hand prostheses

Assessment of Myoelectric Controller Performance and Kinematic Behavior of a Novel Soft Synergy-Inspired Robotic Hand for Prosthetic Applications

abstract: Myoelectric artificial limbs can significantly advance the state of the art in prosthetics, since they can be used to control mechatronic devices through muscular activity in a way that mimics how the subjects used to activate their muscles before limb loss. However, surveys indicate that dissatisfaction with the functionality of terminal devices underlies the widespread abandonment of prostheses. We believe that one key factor to improve acceptability of prosthetic devices is to attain human likeness of prosthesis movements, a goal which is being pursued by research on social and human–robot interactions. Therefore, to reduce early abandonment of terminal devices, we propose that controllers should be designed so as to ensure effective task accomplishment in a natural fashion. In this work, we have analyzed and compared the performance of three types of myoelectric controller algorithms based on surface electromyography to control an underactuated and multi-degrees of freedom prosthetic hand, the SoftHand Pro. The goal of the present study was to identify the myoelectric algorithm that best mimics the native hand movements. As a preliminary step, we first quantified the repeatability of the SoftHand Pro finger movements and identified the electromyographic recording sites for able-bodied individuals with the highest signal-to-noise ratio from two pairs of muscles, i.e., flexor digitorum superficialis/extensor digitorum communis, and flexor carpi radialis/extensor carpi ulnaris. Able-bodied volunteers were then asked to execute reach-to-grasp movements, while electromyography signals were recorded from flexor digitorum superficialis/extensor digitorum communis as this was identified as the muscle pair characterized by high signal-to-noise ratio and intuitive control. Subsequently, we tested three myoelectric controllers that mapped electromyography signals to position of the SoftHand Pro. We found that a differential electromyography-to-position mapping ensured the highest coherence with hand movements. Our results represent a first step toward a more effective and intuitive control of myoelectric hand prostheses.View the article as published at http://journal.frontiersin.org/article/10.3389/fnbot.2016.00011/ful

A Physical Human–Robot Interaction Framework for Trajectory Adaptation Based on Human Motion Prediction and Adaptive Impedance Control

Physical human-robot interaction (pHRI) plays an important role in robotic. In order for a human operator to be able to easily adapt to interact with a robot, a minimal interaction force in pHRI should be achieved. In this paper, a pHRI framework is proposed to allow the robot to regulate its trajectory adaptively for minimizing the interaction force with small position-tracking errors. The trajectory of the robot is first adjusted by the interaction force which is updated by the performance evaluation index. Then, the human hand motion is predicted based on the autoregressive (AR) model to further adapt the trajectory. Thirdly, an adaptive impedance control method is developed to update the stiffness in the robot impedance controller using surface electromyography (sEMG) signals for robot compliant interaction with the environment. This method allows the human operator to interact with the robot by the interaction force, the hand motion and muscle contraction. By investigating the performance of the proposed method, the interaction force is decreased and a good position tracking accuracy is achieved. Comparative experiments demonstrate the enhanced performance of the proposed method. Note to Practitioners —This paper focuses on developing a novel method that can allow the robot to compliantly interact with the human operator while simultaneously taking into account the trajectory-tracking accuracy and the interaction force in pHRI scenarios. The proposed method has a large application potential in a variety of pHRI tasks, such as human-robot collaborative transporting, curing, assembly, cutting, and so on. In addition, the proposed method can allow the human operator to physically interact with the robot in an easier and more intuitive manner, by taking advantage of human motion prediction and adaptive impedance control. Therefore, it is also potentially utilized for rehabilitation and assistive robots, and robot learning skills from human physical demonstration

Robotic impedance learning for robot-assisted physical training

Impedance control has been widely used in robotic applications where a robot has physical interaction with its environment. However, how the impedance parameters are adapted according to the context of a task is still an open problem. In this paper, we focus on a physical training scenario, where the robot needs to adjust its impedance parameters according to the human user's performance so as to promote their learning. This is a challenging problem as humans' dynamic behaviors are difficult to model and subject to uncertainties. Considering that physical training usually involves a repetitive process, we develop impedance learning in physical training by using iterative learning control (ILC). Since the condition of the same iteration length in traditional ILC cannot be met due to human variance, we adopt a novel ILC to deal with varying iteration lengthes. By theoretical analysis and simulations, we show that the proposed method can effectively learn the robot's impedance in the application of robot-assisted physical training

A probabilistic framework for learning geometry-based robot manipulation skills

Programming robots to perform complex manipulation tasks is difficult because many tasks require sophisticated controllers that may rely on data such as manipulability ellipsoids, stiffness/damping and inertia matrices. Such data are naturally represented as Symmetric Positive Definite (SPD) matrices to capture specific geometric characteristics of the data, which increases the complexity of hard-coding them. To alleviate this difficulty, the Learning from Demonstration (LfD) paradigm can be used in order to learn robot manipulation skills with specific geometric constraints encapsulated in SPD matrices. Learned skills often need to be adapted when they are applied to new situations. While existing techniques can adapt Cartesian and joint space trajectories described by various desired points, the adaptation of motion skills encapsulated in SPD matrices remains an open problem. In this paper, we introduce a new LfD framework that can learn robot manipulation skills encapsulated in SPD matrices from expert demonstrations and adapt them to new situations defined by new start-, via- and end-matrices. The proposed approach leverages Kernelized Movement Primitives (KMPs) to generate SPD-based robot manipulation skills that smoothly adapt the demonstrations to conform to new constraints. We validate the proposed framework using a couple of simulations in addition to a real experiment scenario

Development of an intelligent robotic system for rehabilitation of upper limbs using a collaborative robot

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáRehabilitation is a relevant process for the recovery from dysfunctions and improves the realisation of patient’s Activities ofDaily Living (ADLs). Therefore, the development of technologies for this field has significant importance because the improvement of the rehabilitation can affect many people. This work proposes a robotic system for the rehabilitation of the upper limbs using a collaborative robot and an intelligent control algorithm that makes the solution robust and adaptable to each patient. The UR3 from Universal Robots© was used to implement two Reinforcement Learning algorithms, the SARSA and Q-learning, applied to this rehabilitation problem. The goal of this system provides a common training force applying resistance on the movement performed by the patient. This thesis is divided into twomain parts. The first one was the development of a simulation composed by the UR3 and a human model in V-REP platformthat could be controlled through a dedicated interface or externally through the MATLAB using the self-control algorithms. This simulation was created with a graphical interface for visualisation, and a human-machine interface, to control the robotic system with RL algorithm, built onMATLAB. The results obtained with the simulation presented the expected system behaviour. The second part was the experiment of the real system with a healthy subject. The experiment was divided in two phases the first considering the training only in one axis and second in the three Cartesian axes. The used algorithms were the same as the simulation, but in this case, they were implemented in Python language. The experiment considering one axis presents satisfactory results, while for the three axes the results were not so good. The obtained results with the real system experiment for one and three axis were compared with the human armmodel proposed in other studies to validate the applied methodology. This work represents an important contribution for the field because presents a new feature to help therapists and patients to get better results in the rehabilitation process.A reabilitação é um processo relevante para a recuperação de disfunções e para uma melhor realização das Atividades de Vida Diária (AVDs) do paciente. Portanto, o desenvolvimento de tecnologias para este campo tem uma importância significativa, pois o aprimoramento da reabilitação pode afetar muitas pessoas. Este trabalho propõe um sistema robótico para a reabilitação dos membros superiores utilizando umrobô colaborativo e umalgoritmo de controle inteligente, o que torna a solução robusta e adaptável para cada paciente. O robô UR3 da Universal Robots© foi usado como base para a implementação de dois algoritmos de Reinforcement Learning (RL), o SARSA e o Q-learning, aplicados a esse problema de reabilitação. O objetivo deste sistema é fornecer umtreinamento de força comum, aplicando uma resistência ao movimento realizado pelo paciente. Basicamente, esta tese está dividida em duas partes principais. A primeira foi o desenvolvimento de uma simulação composta pelo UR3 e um modelo do corpo humano na plataforma V-REP, que pudesse ser controlado através de uma interface dedicada ou, externamente, através do MATLAB usando os algoritmos de Reinforcement Learning. Essa simulação foi criada com uma interface gráfica para visualização e uma interface homem-máquina, para controlar o sistema robótico com o algoritmo RL, construído no MATLAB. Os resultados obtidos com a simulação apresentaram o comportamento esperado do sistema. A segunda parte foi o experimento do sistema real com umindivíduo saudável. O experimento foi dividido em duas fases: a primeira considerando o treinamento apenas em um eixo e a segunda nos três eixos cartesianos. Os algoritmos utilizados foram os mesmos da simulação,mas, neste caso, foramimplementados na linguagem Python. Os resultados são apresentados quer em simulação quer com o robô real e validam a metodologia desenvolvida e aplicada. Os resultados obtidos com o experimento real do sistema para a apenas umeixo foram comparados com a simulação do modelo do braço humano proposto em outros trabalhos para validar ametodologia aplicada. Este trabalho representa uma contribuição importante para o campo da reabilitação, pois apresenta umnovo recurso para ajudar terapeutas e pacientes a obter melhores resultados no processo de reabilitação