Two-link Arm Research Articles

The Most Effective VAF Threshold for Extracting the Optimum Number of Synergies for Reaching Movement in a Two-Link Arm Model with Two DoF

Purpose: Muscle synergy is a motor feature composed of synergy patterns and activation coefficients. This study aimed to combine the two-link arm model with synergy patterns and muscle activation coefficients, which in turn leads to selecting the optimum number of synergies by changing the best Variability Account For (VAF) criterion. Materials and Methods: In this paper, signals were recorded from six arm muscles involved in arm-reaching movement while carrying a certain weight (w=700 g) by 20 subjects. The synergy pattern and activation coefficient matrices were calculated by using the Non-negative Matrix Factorization method (NNMF) and VAF criterion. Subsequently, to find the best VAF threshold, the output of signal preprocessing and NNMF’s output were done on Hill’s model. Results: Average VAF% for 20 subjects in the mentioned movement was 97.34±2.0%, and four numbers of synergies were determined. Conclusion: The results of the study suggest that the output of the W*H matrix (W and H are equal to the synergy pattern matrix and the activation coefficient matrix, in turn) had harmony with the output of the signal matrix recorded from all 20 subjects (output means the endpoint position and theta 1 and theta 2 angles) when they were performed as input on the two-link arm model. This harmony can be seen when choosing the best VAF critical threshold (value≥96%) via the aforementioned procedure. This harmony in turn contributes to exerting a positive influence on optimal extracting synergy patterns and describing the arm-reaching space more clearly.

Series Clutched Actuation for Collision-Tolerant High-Speed Robots

Abstract Collisions at high-speed can severely damage robots with non-backdrivable drivetrains. Adding an overload clutch in series can improve the robot’s collision tolerance without compromising its high dynamic performance. This paper aims at determining the speed above which overload clutches are required in a two-link manipulator arm. Furthermore, the optimal clutch topology as function of the impact velocity is investigated. Third, it is evaluated if adding clutches can lower the impact force on the arm. Finally, the maximum speed is identified below which impact-aware robot control is possible. The latter requires that none of the clutches decouple during an intentional collision with the environment. These answers are obtained through collision simulations and experiments with a custom build two-link arm. It was found that adding a clutch reduces the torque experienced by the drivetrain by an order of magnitude and below the limit momentary peak torque of the strain wave gears that are used. Adding a clutch to the elbow joint of the two-link arm was effective in protecting the shoulder as well if the impact occurred at the tool center point. With respect to a rigid elbow joint, the clutched elbow joint reduced the collision force at the tool by only 8%. To demonstrate that the arm is impact-aware, a box of 8 kg is approached, impacted, and pushed at 1 m/s without decoupling a clutch, nor damaging the robot’s hardware.

Microsurgical Wiltse Paraspinal Approach Using a Low-Budget Exoscope.

Access to microneurosurgical care in low- and middle-income countries remains limited mainly due to a lack of equipment. High purchasing and maintenance costs hinder the use of operating microscopes in low-resource facilities. The authors present an improved version of their previously introduced low-cost exoscope to achieve high magnification and illumination in low-resource environments. The setup included a 48-megapixel two-dimensional digital microscope camera, a wide field C-mount lens, ring light, and a two-link cantilever with a screw terminal. The surgical field was projected to a portable 17.3-inch 2K resolution monitor. Ten patients underwent exoscope-assisted transforaminal lumbar interbody fusion via the Wiltse paraspinal approach. The simple construction allowed a fast and intuitive preoperative setup. The in-plane switching type display provided a clear and bright image regardless of the viewing angle. The two-link arm of the cantilever allowed smooth positioning of the camera, overcoming the cumbersome up and down movements needed to zoom in and out with the previous prototype. Industrial microscope cameras are effective low-budget alternatives to conventional operating microscopes in lumbar microdiscectomy. The improved system is superior compared to the authors' previous prototype with regard to affordability, image quality, and adjustability of position and angle.

Integrated Poser+MATLAB Environment to Enhance Virtual Reality Toolbox Capabilities for Bio-System 3D Animations

Purpose: Although MATLAB is a powerful math program, its virtual reality (VR) toolbox is limited for those who engage in the modelling of human body systems. The integration of Poser software with MATLAB is provided in this study for designing and developing human bodies in VR.
 Materials and Methods: First, a human entire body model was created in the Poser environment. The prototype is then loaded into MATLAB Simulink.
 Results: Two-Link Arm powered by Six Muscles (TLASM), a well-known model of human arm movements in the horizontal plane, is simulated to test the efficiency of the virtual model. TLASM was presented in this team's prior work.
 Conclusion: Despite the fact that only one limb of the human virtual platform body has been tested, it can easily be applied to other limbs.

Design and validation of an MRI-compatible mechatronic system for needle delivery to localized prostate cancer.

Prostate cancer is the most common non-cutaneous cancer among men in the United States and is the second leading cause of cancer death in American men. (Siegel et al. [2019] CA: A Cancer J Clin.69(1):7-34.) Focal laser ablation (FLA) has the potential to control small tumors while preserving urinary and erectile function by leaving the neurovascular bundles and urethral sphincters intact. Accurate needle guidance is critical to the success of FLA. Multiparametric magnetic resonance images (mpMRI) can be used to identify targets, guide needles, and assess treatment outcomes. The purpose of this work was to design and evaluate the accuracy of an MR-compatible mechatronic system for in-bore transperineal guidance of FLA ablation needles to localized lesions in the prostate. The mechatronic system was constructed entirely of non-ferromagnetic materials, with actuation controlled by piezoelectric motors and optical encoders. The needle guide hangs between independent front and rear two-link arms, which allows for horizontal and vertical translation as well as pitch and yaw rotation of the guide with a 6.0cm range of motion in each direction. Needles are inserted manually through a chosen hole in the guide, which has been aligned with the target in the prostate. Open-air positioning error was evaluated using an optical tracking system (0.25mm RMS accuracy) to measure 125 trajectories in free space. Correction of systematic bias in the system was performed using 85 of the trajectories, and the remaining 40 were used to estimate the residual error. The error was calculated as the horizontal and vertical displacement between the axis of the desired and measured trajectories at a typical needle insertion depth of 10cm. MR-compatibility was evaluated using a grid phantom to assess image degradation due to the presence of the system, and induced force, heating, and electrical interference in the system were assessed qualitatively. In-bore positioning error was evaluated on 25 trajectories. Open-air mean positioning error at the needle tip was 0.80±0.36mm with a one-sided 95% confidence interval of 1.40mm. The mean deviation of needle trajectories from the planned direction was 0.14±0.06∘ . In the MR bore, the mean positioning error at the needle tip was 2.11±1.05mm with a one-sided 95% prediction interval of 3.84mm. The mean angular error was 0.49±0.26∘ . The system was found to be compatible with the MR environment under the specified gradient-echo sequence parameters used in this study. A complete system for delivering needles to localized prostate tumors was developed and described in this work, and its compatibility with the MR environment was demonstrated. In-bore MRI positioning error was sufficiently small for targeting small localized prostate tumors.

Stochastic Genetic Algorithm-Assisted Fuzzy Q-Learning for Robotic Manipulators

This work proposes stochastic genetic algorithm-assisted Fuzzy Q-Learning-based robotic manipulator control. Specifically, the aim is to redefine the action choosing mechanism in Fuzzy Q-Learning for robotic manipulator control. Conventionally, a Fuzzy Q-Learning-based controller selects a deterministic action from available actions using fuzzy Q values. This deterministic Fuzzy Q-Learning is not an efficient approach, especially in dealing with highly coupled nonlinear systems such as robotic manipulators. Restricting the search for optimal action to the agent’s action set or a restricted set of Q values (deterministic) is a myopic idea. Herein, the proposal is to employ genetic algorithm as stochastic optimizer for action selection at each stage of Fuzzy Q-Learning-based controller. This turns out to be a highly effective way for robotic manipulator control rather than choosing an algebraic minimal action. As case studies, present work implements the proposed approach on two manipulators: (a) two-link arm manipulator and (b) selective compliance assembly robotic arm. Scheme is compared with baseline Fuzzy Q-Learning controller, Lyapunov Markov game-based controller and Linguistic Lyapunov Reinforcement Learning controller. Simulation results show that our stochastic genetic algorithm-assisted Fuzzy Q-Learning controller outperforms the above-mentioned controllers in terms of tracking errors along with lower torque requirements.

Research on Robotic Arm Movement Grasping System Based on MYO

This paper studies the continuous motion capture system of a robotic arm based on MYO. The MYO sensor can collect the electromyographic signal (sEMG) and the motion signal (IMU) of the arm during exercise, and filter the electromyographic signal, extract the features, and return the features. After the first transformation, the machine learning algorithm is used for model training. In order to verify the effect of the recognition of the EMG signal on the deep learning method, the accuracy comparison experiment with the BP neural network is also added. In addition, a two-link arm model was built, and the pose information of the end of the arm was obtained through Euler angle transformation, and then the end pose information of the robotic arm was obtained through coordinate mapping, and the end pose simulation was performed using Matlab software. The simulation results prove that the performance of the system meets expectations, and points out the direction for subsequent research.

Repetitive Control for Multi-Joint Arm Movements Based on Virtual Trajectories.

According to the neuromuscular model of virtual trajectory control, the postures and movements of limbs are performed by shifting the equilibrium positions determined by agonist and antagonist muscle activities. In this study, we develop virtual trajectory control for the reaching movements of a multi-joint arm, introducing a proportional-derivative feedback control scheme. In virtual trajectory control, it is crucial to design a suitable virtual trajectory such that the desired trajectory can be realized. To this end, we propose an algorithm for updating virtual trajectories in repetitive control, which can be regarded as a Newton-like method in a function space. In our repetitive control, the virtual trajectory is corrected without explicit calculation of the arm dynamics, and the actual trajectory converges to the desired trajectory. Using computer simulations, we assessed the proposed repetitive control for the trajectory tracking of a two-link arm. Our results confirmed that when the feedback gains were reasonably high and the sampling time was sufficiently small, the virtual trajectory was adequately updated, and the desired trajectory was almost achieved within approximately 10 iterative trials. We also propose a method for modifying the virtual trajectory to ensure that the formation of the actual trajectory is identical even when the feedback gains are changed. This modification method makes it possible to execute flexible control, in which the feedback gains are effectively altered according to motion tasks.

Modeling and Control Human Arm With FuzzyGenetic Muscle Model Based on Reinforcement Learning: The Muscle Activation Method

Background: The central nervous system (CNS) is optimizing arm movements to reduce some kind of cost function. Simulating parts of the nervous system is one way of obtaining accurate information about the neurological and treatment of neuromuscular diseases. The primary purpose of this paper is to model and control the human arm in a reaching movement based on reinforcement learning (RL) theory. Methods: First, Zajac’s muscle model has improved by a fuzzy system. Second, the proposed muscle model applied to the 6 muscles, which are responsible for a two-link arm that moves in the horizontal plane. Third, the model parameters are approximated based on the genetic algorithm (GA). Experimental data recorded from healthy subjects for assessing the approach. At last, the RL algorithm has utilized to guide the arm for reaching tasks. Results: The results show that: (1) The proposed system is temporally similar to a real arm movement. (2) The RL algorithm can generate the motor commands obtained from electromyographies (EMGs). (3) The similarity of obtained activation function from the system has compared with the real data activation function, which may prove the possibility of RL in the CNS (basal ganglia). Finally, in order to have a graphical and effective representation of the arm model, the virtual reality environment of MATLAB has been used. Conclusion: Since the RL method is a representative of the brain’s control function, it has some features, such as better settling time, not having any peek overshoot, and robustness.

Combination of reinforcement learning and bee algorithm for controlling two-link arm with six muscle: simplified human arm model in the horizontal plane.

The aim of this study was to improve reinforcement learning algorithm by combining artificial bee colony algorithm. The traditional method of reinforcement learning algorithm has a very low convergence rate due to random choices. An ant algorithm will help to make random choices in reinforcement learning more appropriate. This hybrid algorithm called the bee colony reinforcement (BCR) algorithm. The tip of the arm must reach a predetermined purpose by BCR algorithm. The results show that the BCR algorithm in the model has been able to reduce the time to reach the goal than the reinforcement learning algorithm (In average 12 steps faster). Also, the path for reaching the goal in the BCR algorithm was far more direct and shorter than the reinforcement learning algorithm. This method also detects the optimal path towards the goal.

Tracking Fault Tolerant Control for Hybrid System: Two-Link Human Arm Application

This paper proposes a novel fault tolerant control (FTC) scheme for a class of hybrid dynamical system (HDS) subject to sensor faults. The corresponding FTC architecture is designed around a reconfiguration mechanism. It aims to compensate the effects of the sensors degradation and maintain satisfactory performances including continuous stability. Moreover, by using the linear matrix inequalities (LMI) approach, a fault estimation algorithm is fulfilled and the compromise between robustness to disturbances and sensitivity to fault is guaranteed. For the sake of trajectory tracking, a combined robust state feedback and proportional-integral-derivative control system is proposed herein. Finally, extensive simulation results conducted on two-link arm system are included to illustrate the efficiency of the designed FTC scheme.

Distributed adaptive leader-following tracking control of networked Lagrangian systems with unknown control directions under undirected/directed graphs

ABSTRACTThis paper addresses the leader-following consensus problem of networked Lagrangian systems with unknown control directions and uncertain dynamics. For undirected graphs and directed graphs, two types of distributed control protocols are proposed without assuming that the leader's position information is linearly parameterised. It is proven that all signals in the closed-loop system are bounded, and a leader-following consensus can be achieved with the proposed corresponding protocols. These protocols are distributed in the sense that the control input for each Lagrangian system is solely based on local relative position and velocity information from its neighbourhood set and does not require additional information, e.g. acceleration or observer information of its neighbours, thus avoiding the dead-loop problem and reducing the communication burden. Simulations on networked two-link revolute joint arms are given to validate the theoretical findings.

Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network.

The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedforward and recurrent connections in a network of heterogeneous spiking neurons. The error in the output is fed back through fixed random connections with a negative gain, causing the network to follow the desired dynamics. The rule for Feedback-based Online Local Learning Of Weights (FOLLOW) is local in the sense that weight changes depend on the presynaptic activity and the error signal projected onto the postsynaptic neuron. We provide examples of learning linear, non-linear and chaotic dynamics, as well as the dynamics of a two-link arm. Under reasonable approximations, we show, using the Lyapunov method, that FOLLOW learning is uniformly stable, with the error going to zero asymptotically.

Towards a Cybernetic Model of Human Movement

An elementary computational framework, as a first step to an eventual comprehensive model of human movement, is presented. Such a model in conjunction with anatomical, physiological and experimental studies should provide a means of verifying theoretical, experimental, and heuristic models of human movement. For this purpose, a three-dimensional three link humanoid model and a two-link planar arm model are presented to explore responses to simple external forces. Such models are useful for a variety of current applications in art, science, engineering, sports, and medicine. The models are subjected to kinesthetic, auditory, and visual inputs. Creating desired behavior is the goal. The models are flexible, modular, and expandable for inclusion of more segments, muscles, and sensory and central nervous system (CNS) processing. Three computer simulations are presented: rhythmic maneuvers of the three link model, in response to periodic motion of a platform, the planar arm producing visually observed alphabet-like characters in response to visual inputs, and processing of music to provide rhythms for a three-link dance.

Simulation-Based Analysis of a Linear Switched System Based on Human Arm’s Dynamics

The article outlines the process of developing a mathematical model of the human arm. The model arm consists of two joints connected by the rotational link. Each of them is represented as a truncated cone prism which changes its shape during motion. The shape change instant depends on the two-state coordinates being the angular displacements q1 and q2. Based on the analysis of the arm motion a mathematical model is proposed. The design process is made on the basis of switched systems theory. We present a transition from the general equation of a two-link arm’s dynamics to the state equation for stationary linear models. Based on previous step a mathematical model is presented as a switched linear system. The article also shows that the dependency of the switching rule on the two components of the state vector.

Vibration attenuation in two-link flexible mechanical arms with periodic composite materials

We introduce the periodic composite materials, so-called phononic crystals, to the flexible mechanical arms systems. Due to the transfer matrix method and the Bloch theorem, the theoretical solution of band structure of the two-link model is deduced and then verified by the frequency response by the finite element method. The influence of the included angle of arms to vibration characteristics is analyzed. The frequency response of the two-link flexible arms with/without phononic crystals is investigated. The results illustrate that, by using the periodic composite materials, some frequency ranges with strong attenuation can be obtained. This study provides a new way to eliminate vibrations in flexible mechanical arms.

A Control Scheme for Industrial Robots Using Artificial Neural Networks

 Abstract—This paper develops a new model-free control scheme based on artificial neural networks (ANN) for trajectory tracking applied on industrial manipulators. This scheme is developed to control arm robot manipulator without calculate the model parameters or dynamics, and use the online identification instead. The scheme consists of three parts. These parts are inverse identification part, ANN controller and linear controller. Inverse dynamics of the manipulator is identified by recurrent ANN that gives the identified torque. The ANN controller works on controlling the arm robot depends on the identifying torque. The linear controller designed for trajectory tracking error regulation. The identification and control ANN work together to improve the response of the linear controller. A simulated two-link arm robot is used to apply the control scheme on it. The scheme verified by mass variation. A comparison between the response of the manipulator with linear controller only and with the fully scheme has been carried out. The results show that adding the identification and control ANN improve the results of the linear controller.

Stable Control of Under-Actuated Mechanical System Acrobot

The mechanical system with one control input and two degrees of freedom is one kind of simple under-drive system, As a typical of two-DOF underactuated mechanical systems , Two-link underactuated mechanical arms is a system scholars always research of.This paper mainly take the two link underactuated mechanical arms-Acrobot which motivate in the vertical plane as object to discusses the asymptotic stability of control

Motion control of industrial robots by considering serial two-link robot arm model with joint nonlinearities

This paper presents a model based motion control approach for industrial robots by considering a serial two-link robot arm model with joint nonlinearities. In order to achieve the desired performance using the model based control approaches, it is important to obtain relevant models of both kinematics and dynamics including nonlinear characteristics. Main nonlinear components that lead to trajectory tracking errors of typical multi-axis industrial robot are joint nonlinearities in each axis and dynamic coupling effects between different axes. In this paper, a parametric modeling approach is introduced to reproduce behaviors of a serial two-link robot arm with joint nonlinearities. Nonlinear stiffness, angular transmission errors, and friction in these two links are directly identified as joint nonlinearities. This approach is applied for the serial two-link arm of a typical multi-axis industrial robot, which has low frequency vibration modes and significantly affects to the trajectory performance. Effectiveness of the modeling is verified by comparative studies with numerical simulations and experiments. Finally, a 2-DOF control scheme with the identified two-link dynamic model and a feedback loop-shaping with a variable notch filter are applied to improve the performance of trajectory tracking and residual vibration suppression.

Neural-Network-Friction Compensation-Based Energy Swing-Up Control of Pendubot

This paper proposes the energy-based controller (EC) incorporated with radical basis function (RBF) neural-network compensation (ECRBFC), which is used to swing up the Pendubot and raise it to its uppermost unstable equilibrium position. First, for the known dynamics model of the two-link arm, the EC is designed. In the EC, the singularity is successfully avoided by constructing an appropriate energy evaluation function. Second, as for the friction of the Pendubot, because of the time-varying characteristics, an accurate friction dynamics model cannot be known absolutely; thus, the RBF neural network is introduced to offset the bad effect of friction. Finally, in order to evaluate the performance of ECRBFC, the numerical simulations and the experimental results are given, and by comparing the results with that of other algorithms, it is found that ECRBFC proposed in this paper has better performance under the same conditions.