Modeling and Verification of Humanoid Robot Task Coordination

The main objectives of the present work are to model and implement a control system of five degrees of freedom (5 DOF) humanoid arm by using an exoskeleton system with 3 DOF. The exoskeleton system is used to transport the movements of the human’s arm to the controller to control the humanoid arm by using sensors at each target joint to measure the angular displacement of it. To achieve this target, a kinematic analysis of the 5 DOF arm robot has been carried out including the forward, and solving the inverse kinematics. In addition, heterogeneous teleoperation based on forward and inverse kinematic between the humanoid arm and the exoskeleton model has been performed. Also, an experimental set up has been arranged to include the sensors considered (angular displacement sensors), control unit, signal-processing boards, and the actuators (servo motors of the humanoid arm robots). The control experiments have been conducted based on PID control. Keywords : Forward kinematic, inverse kinematic, teleoperation, humanoid robot Cite this Article Elsamanty M, Sayed M, Abdr Aboo S. Heterogeneous Teleoperation Model for Controlling 3D Printed Humanoid Robot Arm with 5 DOF using 3 DOF Exoskeleton. Journal of Control & Instrumentation. 2019; 10(2): 1–15p.

Humanoid robots are similar in dimension and degrees of freedom to humans. Compared with other types of robots, humanoid robots are more adaptable to complex living environment and various tools of humans. The combination of wheeled robot and humanoid robot is an effective way to increase the moving speed, range of motion and environmental adaptability of humanoid robots, which can accelerate the application of humanoid robots in human living environments. In this way, the use of humanoid robots in industry and services can be effectively improved. This article introduces a novel electrically-driven wheel-legged humanoid robot BHR-W with 14 degrees of freedom. It can use wheels to achieve rapid motion on structured pavement. In addition, BHR-W has two degrees of freedom of hip joint, giving it excellent ability to perform complex actions such as walking, jumping and roller skating to cross unstructured environments. BHR-W is an innovative design which enables humanoid robots to have capabilities of bipedal walking and wheel rolling. This article introduces the mechanical and system design of the robot, and designs a controller for balance control. Finally, the stability and reliability of the system and the feasibility of the balance control strategies are verified by several practical experiments.

Motions reinforce meanings in human-robot communication, when they are relevant and initiated at the right times. Given a task of using motions for an autonomous humanoid robot to communicate, different sequences of relevant motions are generated from the motion library. Each motion in the motion library is stable, but a sequence may cause the robot to be unstable and fall. We are interested in predicting if a sequence of motions will result in a fall, without executing the sequence on the robot. We contribute a novel algorithm, ProFeaSM, that uses only body angles collected during the execution of single motions and interpolations between pairs of motions, to predict whether a sequence will cause the robot to fall. We demonstrate the efficacy of ProFeaSM on the NAO humanoid robot in a real-time simulator, Webots, and on a real NAO and explore the trade-off between precision and recall.

Problem statement: Present researches focus to make humanoid robots more and more autonomous so they can assist human in daily works like taking care of children, aged or disabled persons. In such social activities, the contemporar y humanoid robots are expected to have human like morphology and gait. Studies on bipedal locomotion for humanoid robots are then part of the hottest topics in the field of robotic researches. Knowing the benefits of arm swinging for human gait, we propose in this study a new prototype of female humanoid robot morphology having the capabilities to swing arms during step walking. Approach: A new humanoid robot prototype had been introduced based on a human morphology corresponding to a woman whose weight is 70 kg and height is 1,73 m and using realistic gait parameters of a women. The female hu manoid robot prototype was composed of fifteen links associated to twenty-six degrees of freedom. Winter statistical model had been applied to determ ine all physical parameters corresponding to each link. Modeling the proposed humanoid robot implies first to establish the kinematic model basically founded on Euler's transformation matrix and then to set th e dynamic model computed using the Newton-Euler method. To show how the arms played an important role in bipedal gait, we had chosen to consider the whole body as two independent robotic systems: the upper body and the lower body. Results: Both three dimensional kinematic and dynamic models of the humanoid robot had been developed. The three dimensional humanoid robot was controlled via a feedback linearization control during the single su pport, impact and double support phases. The simulation results showed the arm swing during the step of walking. Conclusion: The humanoid robot proposed has a human like morphology and ensures the function of a step walking with arm swinging. The applied control laws have ensured to the robot desired performances during a step walking.

While recent advances in approaches for control of humanoid robot systems show highly promising results, consideration of fully integrated humanoid systems for solving complex tasks such as disaster response has only gained focus recently. In this work, a software framework for humanoid disaster response robots is introduced. It provides newcomers as well as experienced researchers in humanoid robotics a comprehensive system comprising open source packages for locomotion, manipulation, perception, world modeling, behavior control, and operator interaction. The system uses the Robot Operating System (ROS) as a middleware, which has emerged as a de-facto standard in robotics research in recent years. The described architecture and components allow for flexible interaction between operator(s) and robot from teleoperation to remotely supervised autonomous operation while considering bandwidth constraints. The components are self-­contained and can be used either in combination with others or standalone. They have been developed and evaluated during participation in the DARPA Robotics Challenge and their use for different tasks and parts of this competition are described.

The understanding of how humans process information, determine salience, and combine seemingly unrelated information is essential to automated processing of large amounts of information that is partially relevant, or of unknown relevance. Recent neurological science research in human perception, and in information science regarding contextbased modeling, provides us with a theoretical basis for using a bottom-up approach for automating the management of large amounts of information in ways directly useful for human operators. However, integration of human intelligence into a game theoretic framework for dynamic and adaptive decision support needs a perception and cognition model. For the purpose of cognitive modeling, we present a brain-computer-interface (BCI) based humanoid robot system to acquire brainwaves during human mental activities of imagining a humanoid robot-walking behavior. We use the neural signals to investigate relationships between complex humanoid robot behaviors and human mental activities for developing the perception and cognition model. The BCI system consists of a data acquisition unit with an electroencephalograph (EEG), a humanoid robot, and a charge couple CCD camera. An EEG electrode cup acquires brainwaves from the skin surface on scalp. The humanoid robot has 20 degrees of freedom (DOFs); 12 DOFs located on hips, knees, and ankles for humanoid robot walking, 6 DOFs on shoulders and arms for arms motion, and 2 DOFs for head yaw and pitch motion. The CCD camera takes video clips of the human subject's hand postures to identify mental activities that are correlated to the robot-walking behaviors. We use the neural signals to investigate relationships between complex humanoid robot behaviors and human mental activities for developing the perception and cognition model.

A Relevant Reduction Method for Dynamic Modeling of a Seven-linked Humanoid Robot in the Three-dimensional Space

The complexity of the kinematic and dynamic structure of humanoid robots make conventional analytical approaches to control increasingly unsuitable for such systems. Learning techniques offer a possible way to aid controller design if insufficient analytical knowledge is available, and learning approaches seem mandatory when humanoid systems are supposed to become completely autonomous. While recent research in neural networks and statistical learning has focused mostly on learning from finite data sets without stringent constraints on computational efficiency, learning for humanoid robots requires a different setting, characterized by the need for real-time learning performance from an essentially infinite stream of incrementally arriving data. This paper demonstrates how even high-dimensional learning problems of this kind can successfully be dealt with by techniques from nonparametric regression and locally weighted learning. As an example, we describe the application of one of the most advanced of such algorithms, Locally Weighted Projection Regression (LWPR), to the on-line learning of three problems in humanoid motor control: the learning of inverse dynamics models for model-based control, the learning of inverse kinematics of redundant manipulators, and the learning of oculomotor reflexes. All these examples demonstrate fast, i.e., within seconds or minutes, learning convergence with highly accurate final peformance. We conclude that real-time learning for complex motor system like humanoid robots is possible with appropriately tailored algorithms, such that increasingly autonomous robots with massive learning abilities should be achievable in the near future.

This article describes the methods of modeling the complex motion of heated billets in induction plants to improve the efficiency of the mathematical models. To ensure the uniformity of heating is necessary to use different types of movement of the workpiece. This can be a rotating or reciprocating motion of the workpiece, the translational motion of the workpiece column. Often requires a combination of several types of movement. Modeling such processes leads to excessive demands on computing resources. In the present article several methods of motion replacement by change of physical parameters or replacement of one complex motion by several simple ones are given. This can significantly increase the efficiency of computational experiments.

Cortical correlate of the Piper rhythm in humans. J. Neurophysiol. 80: 2911-2917, 1998. The electromyogram (EMG) of healthy humans demonstrates a tendency to rhythmic oscillations at around 40 Hz (the Piper rhythm) during strong voluntary contraction. Why motor units should discharge synchronously locked to such a high-frequency is unclear. We recorded whole scalp magnetoencephalographic (MEG) signals simultaneously with surface EMG from 10 healthy subjects. In eight subjects, coherence and time domain analyses demonstrated correspondence between the MEG signal, originating near or in the hand region of the motor cortex, and the 35- to 60-Hz EMG recorded during repeated maximal isometric contractions of the contralateral forearm extensor muscles. Three of these subjects also showed similar coherence during isometric contractions of moderate strength and slow extension movements of the wrist. In addition, coherence and time domain analyses demonstrated correspondence between the MEG signals originating near or in the foot area of the motor cortex and EMG recorded during repeated maximal isometric contractions of the contralateral tibialis anterior muscle in the 30- to 60-Hz range. Most important, the frequency at the peak of the coherence spectrum differed between forearm and leg by as much as 10 Hz in the same subject. In contrast, the peak of the coherence spectrum occurred during sustained weak contraction in the 20- to 30-Hz range similarly for both forearm and foot. The lag between EMG and MEG activity in the leg was approximately 15 ms greater than that seen in the forearm, an interval appropriate for conduction in fast pyramidal pathways. It is concluded that the Piper rhythm in muscle may be driven by a comparable oscillatory activity in the contralateral motor cortex. This cortical rhythmicity can be picked up in several types of movement and seems distinct from the 20- to 30-Hz rhythmicity recorded during weak sustained contractions.

The application of the parallel mechanism is still limited in the humanoid robot fields, and the existing parallel humanoid robot joint has not yet been reflected the characteristics of the parallel mechanism completely, also failed to solve the problem, such as small workspace, effectively. From the structural and functional bionic point of view, a three degrees of freedom(DOFs) spherical parallel mechanism for the shoulder complex of the humanoid robot is presented. According to the structure and kinetic characteristics analysis of the human shoulder complex, 3-PSS/S(P for prismatic pair, S for spherical pair) is chosen as the original configuration for the shouder complex. Using genetic algorithm, the optimization of the 3-PSS/S spherical parallel mechanism is performed, and the orientation workspace of the prototype mechanism is enlarged obviously. Combining the practical structure characteristics of the human shouder complex, an offset output mode, which means the output rod of the mechanism turn to any direction at the point a certain distance from the rotation center of the mechanism, is put forward, which provide possibility for the consistent of the workspace of the mechanism and the actual motion space of the human body shoulder joint. The relationship of the attitude angles between different coordinate system is derived, which establishs the foundation for the motion descriptions under different conditions and control development. The 3-PSS/S spherical parallel mechanism is proposed for the shoulder complex, and the consistence of the workspace of the mechanism and the human shoulder complex is realized by the stuctural parameter optimization and the offset output design.

Vision system is very important for a humanoid robot to work in the human working and living environments. This paper discusses a humanoid robot binocular stereo visual system, with two computers the humanoid robot will perfect realise target object location and manipulation in real-time, the two computers are linked with each other through memolink communication module. One computer is responsible for processing the object images information, the other is used to control humanoid robot to walk and manipulate. An active humanoid robot vision model is built; the 3D location principle and the calibration method of this model are analysed. This active robot vision system with two degrees of freedom enlarges its visual field and the binocular stereo vision system is the most simple camera configuration for 3D position information. Experiments show the effectiveness and robustness of the stereo visual system for a humanoid robot.

Humanoid robots have evolved into a key technology required for many applications which are beyond the reach of humans. As these robots have high number degrees of freedom, a conventional control system is hard to develop. Tele-operation of these humanoid robots by humans is one area which has drawn a lot of interest from the researchers in recent times. This paper proposes a method to imitate the physical movements of multiple persons using Kinect V2 developed by Microsoft. Two main issues have been addressed in this paper. One is finding out the position of speaker when a verbal command is given and the other is to track the body of the speaker and mimic. Multi — microphone array in Kinect V2 has been used for speech recognition and to point the direction of the speaker. Once a mimic command is given by a person, the location of his/her body is obtained and the skeletal data of this person would be used to imitate. The skeletal data of the person being tracked will contain 25 three dimensional coordinates which are the positions of various joints in the body with respect to Kinect V2. Angles at various joints of the skeleton are extracted and given to the microcontroller which controls the humanoid robot. Humanoid robots are not very much stable, whenever the posture of the robot changes there is every chance that the robot may go out of balance. Hence a Zero Moment Point (ZMP) based balance algorithm is implemented for the balancing of the robot.

This study aimed to automate the hammering action and dual arm coordination of a humanoid robot using percussion instruments such as musical saw. In this report, we examined the hammering motion of the robot by using its elastic stiffness, the vibration reduction method of the hitting tool caused by the movement of the robot arm, and dual arm cooperative motion based on hammering sound feedback. The experimental results show that the humanoid robot can play the musical saw without making an S-shaped curve, as performed by humans. By considering these nonlinearities through the mathematical expression of the relationship among frequency, deflection, length of beam, and fulcrum deviation, an arbitrary scale can be generated in the saw. Based on the hammering task and acoustic recognition system, we developed an impact sound feedback system. It was possible to converge to the target frequency of the musical saw through the hammering sound feedback operation.

This paper proposes an intuitive real-time robot control system using human body movement. Recently, it has been developed that motion generation for humanoid robots with reflecting human body movement, which is measured by a motion capture. However, in the existing studies about robot control system by human body movement, the detailed structure information of a robot, for example, degrees of freedom, the range of motion and forms, must be examined in order to calculate inverse kinematics. In this study, we have proposed Associative Motion Generation as humanoid robot motion generation method which does not need the detailed structure information. The associative motion generation system is composed of two neural networks: nonlinear principal component analysis and Jordan recurrent neural network, and the associative motion is generated with the following three steps. First, the system learns the correspondence relationship between an indication and a motion using training data. Second, associative values are extracted for associating a new motion from an unfamiliar indication using nonlinear principal component analysis. Last, the robot generates a new motion through calculation by Jordan recurrent neural network using the associative values. In this paper, we propose a real-time humanoid robot control system based on Associative Motion Generation, that enables user to control motion intuitively by human body movement. Through the task processing and subjective evaluation experiments, we confirmed the effective usability and affective evaluations of the proposed system.