Peidong Liang

Implementation and Test of Human-Operated and Human-Like Adaptive Impedance Controls on Baxter Robot

This paper presents an improved method to teleoperate impedance of a robot based on surface electromyography (EMG) and test it experimentally. Based on a linear mapping between EMG amplitude and stiffness, an incremental stiffness extraction method is developed, which uses instantaneous amplitude identified from EMG in a high frequency band, compensating for non-linear residual error in the linear mapping and preventing muscle fatigue from affecting the control. Experiments on one joint of the Baxter robot are carried out to test the approach in a disturbance attenuation task, and to compare it with automatic human-like impedance adaptation. The experimental results demonstrate that the new human operated impedance method is successful at attenuating disturbance, and results similarly to as automatic disturbance attenuation, thus demonstrating its efficiency.

An Augmented Discrete-Time Approach for Human-Robot Collaboration

Human-robot collaboration (HRC) is a key feature to distinguish the new generation of robots from conventional robots. Relevant HRC topics have been extensively investigated recently in academic institutes and companies to improve human and robot interactive performance. Generally, human motor control regulates human motion adaptively to the external environment with safety, compliance, stability, and efficiency. Inspired by this, we propose an augmented approach to make a robot understand human motion behaviors based on human kinematics and human postural impedance adaptation. Human kinematics is identified by geometry kinematics approach to map human arm configuration as well as stiffness index controlled by hand gesture to anthropomorphic arm. While human arm postural stiffness is estimated and calibrated within robot empirical stability region, human motion is captured by employing a geometry vector approach based on Kinect. A biomimetic controller in discrete-time is employed to make Baxter robot arm imitate human arm behaviors based on Baxter robot dynamics. An object moving task is implemented to validate the performance of proposed methods based on Baxter robot simulator. Results show that the proposed approach to HRC is intuitive, stable, efficient, and compliant, which may have various applications in human-robot collaboration scenarios.

Writing skills transfer from human to robot using stiffness extracted from sEMG

Studies of human motor behaviors have shown that central neural system (CNS) is able to adapt force and impedance in order to optimally interact with interactive environment. Muscle activities regulated by CNS can be represented by surface electromyography (sEMG) measured by electrodes attached on the skin. Inspired by the idea that muscle impedance adaptation reflects motion skills, sEMG based human-robot skill transfer, in particular, the writing skill transfer has been developed based on impedance adaptation extracted from sEMG. Squaring and low-pass filtering based signal envelop extraction algorithm and as well as re-sampling method are employed to extract incremental smooth stiffness from sEMG signals which is then transferred to robot to mimic human motor behavior. The effect of the proposed sEMG based bio-control is evaluated by writing task in comparison with constant stiffness control. Results show that sEMG based human skill transfer has significant effectiveness for skills transfer between human and robot, and it has a great potential to be used in teleoperation.

Development of a hybrid motion capture method using MYO armband with application to teleoperation

In this paper, we have developed a motion capture method based on data collected by MYO armband. The method can be applied on any healthy operator wearing two MYO armbands on both upper and lower arms, respectively. The first MYO armband is worn near the centre of the operators upper arm, the other is worn near the centre of the forearm. MYO armband has built-in eight bioelectrical sensors as well as a 9-axis IMU. The IMU sensors of the MYO are used to detect and reconstruct physical motion of shoulder and elbow joints, while the bioelectrical sensors are used to collect electromyography (EMG) signals associated with wrist motion. This hybrid method enable us to fully capture the motion of the 6-DOF (degree of freedom) of the arm. To test the proposed method, hardware-in-loop simulations studies are performed, with both physiological and physical signals received and processed in MATLAB/Simulink via a low-power bluetooth interface. Results demonstrate the validness and effectiveness of the proposed method.

Development of Kinect based teleoperation of Nao robot

In this paper, an online tracking system has been developed to control the arm and head of a Nao robot using Kinect sensor. The main goal of this work is to achieve that the robot is able to follow the motion of a human user in real time to track. This objective has been achieved using a RGB-D camera (Kinect v2) and a Nao robot, which is a humanoid robot with 5 degree of freedom (DOF) for each arm. The joint motions of the operators head and arm in the real world captured by a Kinect camera can be transferred into the workspace mathematically via forward and inverse kinematics, realitically through data based UDP connection between the robot and Kinect sensor. The satisfactory performance of the proposed approaches have been achieved, which is shown in experimental results.

Teaching by demonstration on dual-arm robot using variable stiffness transferring

Teaching by demonstration (TbD) techniques have been extensively investigated in the recent decades to enable transferring various task skills from human to robots. The traditional TbD techniques focus on teaching motion trajectories that may be sufficient for routine tasks with fixed objects. While for interactive tasks in contact with dynamic environment and objects, e.g., the payload of a robot manipulator may change from one to another, teaching robot only by motion demonstration may cause undesired contact force and inefficiency in the task execution. In this paper, we present a novel TbD method enhanced by transferring the stiffness profile during human robot interaction (HRI). The method is developed on a bimanual robot, whereas one slave arm plays the role of the tutee, and the other master arm coupled with human demonstrator plays the role of tutor. A rendering algorithm is employed to provide demonstrator with force feedback via a purposely built coupling device according to the motion disparity between the two arms. The muscle surface electromyography (sEMG) signals collected during HRI is processed to extract the demonstrators variable stiffness as well as hand grasping patterns. Comparative tests have been carried out on a bimanual Baxter robot for a lifting task with three different set-ups: i) TbD with predefined fixed stiffness; ii) TbD with demonstrator transferred variable stiffness without force feedback; and iii) TbD with demonstrator transferred variable stiffness with force feedback. Results show that the proposed TbD method performs best by transferring the demonstrators physical interactive skill to the robot in a natural and efficient manner.

Development of a robotic teaching interface for human to human skill transfer

The tutor-tutee hand-in-hand teaching may be the most effective approach for a tutee to acquire new motor skills. Repetitive nature of such procedures in a group setting usually results in a high labour cost and time inefficiency. Potential solution can be utilizing robotic platforms playing the role of tutors for demonstrating and transferring the required skills. This requires an appropriate guidance scheme to integrate the tutors motor functionalities into the robots control architecture. For instance, for hand-in-hand supervision of the writing task, the tutors corrections can be applied when necessary, while a very compliant motion can be achieved if no errors are detected. Inspired by this behavior, we develop a teaching interface using a dual-arm robotic platform. In our setup, one arm is connected to the tutees arm providing guidance through a variable stiffness control approach, and the other to the tutor to capture the motion and to feedback the tutees performance in a haptic manner. The reference stiffness for the tutors arm stiffness is estimated in real-time and replicated by the tutees robotic arm. Comparative experiments have been carried out on a dual-arm Baxter robot. The results imply that the human tutor is able to intuitively transfer writing skills to the tutee and also show superior learning performance over over some conventional teaching by demonstration techniques.

A Discrete-Time Algorithm for Stiffness Extraction from sEMG and Its Application in Antidisturbance Teleoperation

We have developed a new discrete-time algorithm of stiffness extraction from muscle surface electromyography (sEMG) collected from human operator’s arms and have applied it for antidisturbance control in robot teleoperation. The variation of arm stiffness is estimated from sEMG signals and transferred to a telerobot under variable impedance control to imitate human motor control behaviours, particularly for disturbance attenuation. In comparison to the estimation of stiffness from sEMG, the proposed algorithm is able to reduce the nonlinear residual error effect and to enhance robustness and to simplify stiffness calibration. In order to extract a smoothing stiffness enveloping from sEMG signals, two enveloping methods are employed in this paper, namely, fast linear enveloping based on low pass filtering and moving average and amplitude monocomponent and frequency modulating (AM-FM) method. Both methods have been incorporated into the proposed stiffness variance estimation algorithm and are extensively tested. The test results show that stiffness variation extraction based on the two methods is sensitive and robust to attenuation disturbance. It could potentially be applied for teleoperation in the presence of hazardous surroundings or human robot physical cooperation scenarios.

SVM based simultaneous hand movements classification using sEMG signals

Prediction of motion volitions is a practical issue in control of artificial limbs. Four classifiers are investigated in this paper to discriminate simultaneous hand movements based on pattern recognition of surface electromyographic (sEMG) signals. A sEMG signal processing tube composed of feature extraction, feature reduction and movements classification is proposed for offline myoelectric pattern recognition. Previous research was mainly devoted to individual hand movements classification. In this paper, several common tools are used for definition of movements. The results show that Support Vector Machine (SVM) outperforms the other three classifiers on both accuracy and model-training time. The user-depend classification accuracy reaches as high as 92.25% while the accuracy of user-independent is about 80%. The proposed classification method is a promising candidate to be used in prosthetic control for a rehabilitation robot in the future.

Optimization design of drive system for industrial robots based on dynamic performance

Purpose The global performance of industrial robots partly depends on the properties of drive system consisting of motor inertia, gearbox inertia, etc. This paper aims to deal with the problem of optimization of global dynamic performance for robotic drive system selected from available components. Design/methodology/approach Considering the performance specifications of drive system, an optimization model whose objective function is composed of working efficiency and natural frequency of robots is proposed. Meanwhile, constraints including the rated and peak torque of motor, lifetime of gearbox and light-weight were taken into account. Furthermore, the mapping relationship between discrete optimal design variables and component properties of drive system were presented. The optimization problem with mixed integer variables was solved by a mixed integer-laplace crossover power mutation algorithm. Findings The optimization results show that our optimization model and methods are applicable, and the performances are also greatly promoted without sacrificing any constraints of drive system. Besides, the model fits the overall performance well with respect to light-weight ratio, safety, cost reduction and others. Practical implications The proposed drive system optimization method has been used for a 4-DOF palletizing robot, which has been largely manufactured in a factory. Originality/value This paper focuses on how the simulation-based optimization can be used for the purpose of generating trade-offs between cost, performance and lifetime when designing robotic drive system. An applicable optimization model and method are proposed to handle the dynamic performance optimization problem of a drive system for industrial robot.