Towards a Smooth Human–Robot Interaction for Rehabilitation Robotic Systems

The goal of this work is to develop a control framework to provide assistance to the subjects in such a manner that the interaction between the subjects and a robot-assisted rehabilitation system is smooth during the rehabilitation therapy. In order to achieve smoothness of interaction, a control framework is designed in such a way that it can automatically adjust the control gains of the robot-assisted rehabilitation system to modify the interaction dynamics between the system and the subject. An artificial neural network (ANN)-based proportional–integral (PI) gain scheduling controller is proposed to automatically determine the appropriate control gains for each individual subject. The human arm model is integrated with the ANN-based PI gain scheduling 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-assisted rehabilitation system could result in smooth interaction. Experimental results involving unimpaired subjects on a PUMA robot-based rehabilitation system are presented to demonstrate the efficacy of the proposed ANN-based PI gain scheduling controller on unimpaired subjects.     

Rehabilitation Robot with Patient-Cooperative Control for Bimanual Training of Hemiparetic Subjects

Hemiparesis is the most common motor deficit following stroke. Bimanual training and robot-assisted therapy are often used to regain motor functionality of the paretic limb. The goal of this study is the development and validation of a bimanual training system that stimulates the use of both arms of hemiparetic subjects. The adaptive assistance control adjusts the contribution of the unaffected arm, thus reducing the load on the paretic arm. Hemiparetic subjects performed three different tracking exercises in bimanual mode and in two unimanual modes to validate the applicability of the system. In bimanual mode, the patient uses the unaffected limb to initiate and guide the movement. The movement of the paretic limb must be coordinated with the unaffected limb to complete the exercise. The training resulted in improvements of motor performance. High and significant correlation between bimanual training and unimanual performance was observed.     

Upper-Limb EMG-Based Robot Motion Governing Using Empirical Mode Decomposition and Adaptive Neural Fuzzy Inference System

To improve the quality of life for the disabled and elderly, this paper develops an upper-limb, EMG-based robot control system to provide natural, intuitive manipulation for robot arm motions. Considering the non-stationary and nonlinear characteristics of the Electromyography (EMG) signals, especially when multi-DOF movements are involved, an empirical mode decomposition method is introduced to break down the EMG signals into a set of intrinsic mode functions, each of which represents different physical characteristics of muscular movement. We then integrate this new system with an initial point detection method previously proposed to establish the mapping between the EMG signals and corresponding robot arm movements in real-time. Meanwhile, as the selection of critical values in the initial point detection method is user-dependent, we employ the adaptive neuro-fuzzy inference system to find proper parameters that are better suited for individual users. Experiments are performed to demonstrate the effectiveness of the proposed upper-limb EMG-based robot control system.     

上肢康复机器人等速运动控制算法的研究

等速运动康复的目标是保证人的手臂在用不断增加的力矩驱动机械臂转动时,机械臂转速始终被控制在预设值不变,且人机的肩关节轴要动态对齐。针对上述要求,通过采集机器人关节伺服电机电枢绕组中的电流值和转子的转速,确定外力矩、电机力矩和转速之间的关系,据此建立等速运动控制算法,并以人手臂肩关节的屈展角度为变量,找出机器臂肩关节轴在高度方向对齐人肩关节轴的函数关系,进而建立了考虑速度和位置双重因素下的人机肩关节轴动态对齐控制算法。通过将上述控制算法用于实物样机的控制,证明了控制算法的正确性。该研究为控制上肢等速运动康复提供了参考。     

A robot safety experiment varying robot speed and contrast with human decision cost

An industrial robot safety experiment was performed to find out how quickly subjects responded to an unexpected robot motion at different speeds of the robot arm, and how frequently they failed to detect a motion that should have been detected. Robotics technicians risk being fatally injured if a robot should trap them against a fixed object. The value of the experimentation lies in its ability to show that this risk can be reduced by a design change. If the robot is moving at a slow speed, during programming and troubleshooting tasks, then the worker has sufficient time to actuate an emergency stop device before the robot can reach the person. The dependent variable in the experiment was the overrun distance (beyond an expected stopping point) that a robot arm travelled before a person actuated a stop pushbutton. Results of this experiment demonstrated that the speed of the robot arm and the implied decision cost for hitting an emergency stop button had a significant effect on human reaction time. At a fairly high level of ambient lighting (560 lux), fixed-level changes in the luminance contrast between the robot arm and its background did not significantly affect human reaction time.     

机器人辅助内镜手术系统的设计与开发

机器人应用于外科手术中，通过医生和机器人系统的合理分工和有机配合，可以提高 手术质量、改善医生工作条件、实现远程手术等．本文以纤维内镜手术为研究对象，针对术 中X射线对医护人员身体健康造成伤害的现状，设计了机器人辅助内镜手术系统，使医生可 以远程控制手术室的操作器完成内镜和手术器械的操作，实现诊断和治疗的目的．文章详细 介绍了系统的研制背景、总体设计、内镜操作器子系统原型样机设计及实验，并结合系统开 发对医疗外科机器人的人机交互接口、安全性及临床应用等问题进行了讨论．     

Development of human-machine interface for teleoperation of a mobile manipulator

Human-robot control interfaces have received increased attention during the past decades for conveniently introducing robot into human daily life. In this paper, a novel Human-machine Interface (HMI) is developed, which contains two components. One is based on the surface electromyography (sEMG) signal, which is from the human upper limb, and the other is based on the Microsoft Kinect sensor. The proposed interface allows the user to control in real time a mobile humanoid robot arm in 3-D space, through upper limb motion estimation by sEMG recordings and Microsoft Kinect sensor. The effectiveness of the method is verified by experiments, including random arm motions in the 3-D space with variable hand speed profiles.     

Adaptive control of a robot arm using driver programs

Most successful state-of-the-art robotic manipulators have the characteristic of producing precise, fast, smooth and reproducible movements. Their drawback is that they tend to have a limited repertoire which can only be extended by costly inverse kinematics calculations or direct teach-in sessions. The goal of our project is to develop a flexible open world robot, which adapts to its task and environment progressively and in an iterative manner. It begins carefully and slowly, with small jagged movements, but, after repetition, reaches an acceptable level of smoothness and execution speed. It can be placed in new surroundings with new task definitions, requiring only a further practice time before becoming expert. Similarities from other sub-tasks are recognized and may be transferred to the new domains. The basic constituents of the adaptive robot are simple iterative inverse kinematics and driver programs. In this paper the idea of driver programs for robot arm control is introduced and their properties investigated.     

基于准确度评估的7自由度手术机器人术前摆位优化算法

针对微创机器人辅助的外科手术复杂的术前摆位问题,以甲状腺微创手术用到的床旁双7自由度协作机器人为研究对象,将机械臂的运动准确度以及运动学性能作为优化目标,提出了机械臂准确度评估指标以及其对应的干涉指数,并提出了一套基于带精英策略的非支配排序遗传算法（NSGA-Ⅱ）多目标遗传算法的术前规划方法。最后,将由该遗传算法优化得...     

Human perception of robot safe speed and idle time

Operators and users of robotic systems perform tasks which require close proximity to dangerous moving parts. Two experiments were performed to assess human perception of safe robot arm speed and idling times. Experiment 1 was designed to determine the maximum safe speed of robots. Subjects were asked to adjust the robot speeds. Perceived safe speeds were indicated for two different types of robots. Experiment 2 was designed to determine safe programmed idle time of robots. Subjects were asked to enter the robot work envelope when a programmed idle was perceived to be caused by a malfunction. Safe idle times were reported for two different robot speeds during operational cycles.     

Real-time method for tip following navigation of continuum snake arm robots

This paper presents a novel technique for the navigation of a snake arm robot, for real-time inspections in complex and constrained environments. These kinds of manipulators rely on redundancy, making the inverse kinematics very difficult. Therefore, a tip following method is proposed using the sequential quadratic programming optimization approach to navigate the robot. This optimization is used to minimize a set of changes to the arrangement of the snake arm that lets the algorithm follow the desired trajectory with minimal error. The information of the Snake Arm pose is used to limit deviations from the path taken. Therefore, the main objective is to find an efficient objective function that allows uninterrupted movements in real-time. The method proposed is validated through an extensive set of simulations of common arrangements and poses for the snake arm robot. For a 24 DoF robot, the average computation time is 0.4 s, achieving a speed of 4.5 mm/s, with deviation of no more than 25 mm from the ideal path.     

Pneumatic impedance control of a 3-d.o.f. physiotherapy robot

Stroke is a common condition resulting in 30 000 people per annum left with significant disability. In patients with severe arm paresis after stroke, functional recovery in the affected arm is poor. Inadequate intensity of treatment is cited as one factor accounting for the lack arm recovery found in some studies. Given that physical therapy resource is limited, strategies to enhance the physiotherapists' efforts are needed. One approach is to use robotic techniques to augment movement therapy. A 3-d.o.f. pneumatic robot has been developed to apply physiotherapy to the upper limb. The robot has been designed with a workspace encompassing the reach-retrieve range of the average male. Slight non-linearities in the response of the pneumatic system are observed and explained. Building upon previous work that used an error-prone custom force sensor, a commercial 6-d.o.f. force sensor is used to apply impedance control in 3 d.o.f. on the robot.     

Online Affect Detection and Robot Behavior Adaptation for Intervention of Children With Autism

Investigation into robot-assisted intervention for children with autism spectrum disorder (ASD) has gained momentum in recent years. Therapists involved in interventions must overcome the communication impairments generally exhibited by children with ASD by adeptly inferring the affective cues of the children to adjust the intervention accordingly. Similarly, a robot must also be able to understand the affective needs of these children—an ability that the current robot-assisted ASD intervention systems lack—to achieve effective interaction that addresses the role of affective states in human–robot interaction and intervention practice. In this paper, we present a physiology-based affect-inference mechanism for robot-assisted intervention where the robot can detect the affective states of a child with ASD as discerned by a therapist and adapt its behaviors accordingly. This paper is the first step toward developing “understanding” robots for use in future ASD intervention. Experimental results with six children with ASD from a proof-of-concept experiment (i.e., a robot-based basketball game) are presented. The robot learned the individual liking level of each child with regard to the game configuration and selected appropriate behaviors to present the task at his/her preferred liking level. Results show that the robot automatically predicted individual liking level in real time with 81.1% accuracy. This is the first time, to our knowledge, that the affective states of children with ASD have been detected via a physiology-based affect recognition technique in real time. This is also the first time that the impact of affect-sensitive closed-loop interaction between a robot and a child with ASD has been demonstrated experimentally.      

Trajectory generation of the writing–brush for a robot arm to inherit block–style Chinese character calligraphy techniques

This paper relates the trajectory generation of the writing-brush for a robot arm to inherit Chinese character calligraphy (CCC) techniques. First, to preserve the characters written by famous calligraphers in Chinese history, we constructed a CCC database which contains 29 456 characters written by different calligraphers in different styles (ancient, angular, block, semi-cursive and cursive style). With this CCC database, it is possible to search, restore and append the calligraphy writing for a specific character. Second, we mainly relate the inheritance of CCC. Because CCC is not a static thing, but a dynamic process of an activity which concerns a lot of complicated factors such as the pressure and speed control of the writing brush, how to write the start and end of strokes, etc., we propose to inherit this dynamic process by a robot. This paper is limited to discussing how to inherit the writing techniques for block-style calligraphy writing by a robot arm. The total number of Chinese characters is more than 800 000. These characters can be constructed by basic strokes. There are 28 different kinds of strokes used to construct all of these characters. The skeleton of a stroke is decided by the control points that exist within the stroke. The shape of a stroke is determined by the trajectory derived from the control points and the pressure to the writing brush. The control points for 28 strokes are given and the control techniques for a robot to write these strokes in block style are developed. At present, the robot can write any character in block style.     

Humanoid robot arm performance optimization using multi objective evolutionary algorithm

As humanoid robots are expected to operate in human environments they are expected to perform a wide range of tasks. Therefore, the robot arm motion must be generated based on the specific task. In this paper we propose an optimal arm motion generation satisfying multiple criteria. In our method, we evolved neural controllers that generate the humanoid robot arm motion satisfying three different criteria; minimum time, minimum distance and minimum acceleration. The robot hand is required to move from the initial to the final goal position. In order to compare the performance, single objective GA is also considered as an optimization tool. Selected neural controllers from the Pareto solution are implemented and their performance is evaluated. Experimental investigation shows that the evolved neural controllers performed well in the real hardware of the mobile humanoid robot platform.     

Multi-body dynamics simulation and gait pattern analysis of a bio-inspired quadruped robot for unstructured terrains using adaptive stroke length

This paper presents the Finite element analysis (FEA), Multi-body dynamics (MBD) simulation and gait pattern analysis of a bio-inspired quadruped robot. A novel power transmission mechanism with varying gear train centre distance has been designed for generating different gait structures to suit unstructured terrain. The adjustable length of the lower link aids in improving the stability of the robot (or) make the robot to travel longer distances within a stipulated time. MBD simulation has been performed using MSC/ADAMS for torque, speed, energy and force analysis. The novel mechanism designed was able to successfully negotiate unstructured obstacles of different heights and these results have been presented in the paper. The results show a direct relationship between stroke length, lift-off distance and gear centre distance. A full scale experimental setup, along with the suitable supporting mechanisms is developed. The experimental analysis has been carried out for measuring the Ground Reaction Force (GRF). The GRF is found to be 45 N on an average, when the robot is walking.     

Feedback control of robot Arm with discrete nonlinear model

This article looks at the problem of controlling a robot arm from a discrete time perspective. We develop the theories and obtain results that feedback linearizes the discrete nonlinear system representing a PUMA 560 robot arm. The effect of sampling on the performance of the arm has been studied and shown through experiments. The results presented here, where the nonlinear robot model has been linearized and controlled completely in discrete time, are new to the robotics literature. We shown certain restrictions necessary on the sampling time of the system. The results obtained have been experimentally verified at the Center for Robotics and Automation at Washington University. Because a number of researchers have addressed the problem of loss of feedback linearizability under sampling, it was important to develop theories and obtain results in discrete time that take into consideration the effects due to sampling. This problem has been addressed here. Analysis of the control law under the assumptions of bounded input is performed and a recursive sensitivity function is derived. The results we obtain in discrete time show the dependency of the performance of the arm on the sampling time. It has been seen that with a higher sampling frequency the performance of the arm substantially improves and it is expected that close to 1000 Hz sampling rate, the peak performance of the PUMA 560 robot arm, will be reached.     

Sensing motion and muscle activity for feedback control of functional electrical stimulation: Ten years of experience in Berlin

After complete or partial paralysis due to stroke or spinal cord injury, electrical nerve stimulation can be used to artificially generate functional muscle contractions. This technique is known as Functional Electrical Stimulation (FES). In combination with appropriate sensor technology and feedback control, FES can be empowered to elicit also complex functional movements of everyday relevance. Depending on the degree and phase of impairment, the goal may be temporary support in a rehabilitation phase, e.g. during re-learning of gait after a stroke, or permanent replacement/support of lost motor functions in form of assistive devices often referred to as neuro-prostheses.In this contribution a number of real-time capable and portable approaches for sensing muscle contractions and motions are reviewed that enable the realization of feedback control schemes. These include inertial measurement units (IMUs), electromyography (EMG), and bioimpedance (BI). This contribution further outlines recent concepts for movement control, which include e.g. cascaded control schemes. A fast inner control loop based on the FES-evoked EMG directly controls the amount of recruited motor units. The design and validation of various novel FES systems are then described that support cycling, walking, reaching, and swallowing. All methods and systems have been developed at the Technische Universität Berlin by the Control Systems Group within the last 10 years in close cooperation with clinical and industrial partners.     

柔性冗余度机器人振动抑制的一种方法

新一代机器人要求具有轻质、高速、重载、高精度以及高度灵活性的 ，上于结构柔引起的振动使实现高精度控制十分困难。研究柔性冗余度机器人就是为了克服现有机器人的不足。     

A neural framework for adaptive robot control

This paper investigates how dynamics in recurrent neural networks can be used to solve some specific mobile robot problems such as motion control and behavior generation. We have designed an adaptive motion control approach based on a novel recurrent neural network, called Echo state networks. The advantage is that no knowledge about the dynamic model is required, and no synaptic weight changing is needed in presence of time varying parameters in the robot. To generate the robot behavior over time, we adopted a biologically inspired approach called neural fields. Due to its dynamical properties, a neural field produces only one localized peak that indicates the optimum movement direction, which navigates a mobile robot to its goal in an unknown environment without any collisions with static or moving obstacles.