Anthropomorphic Manipulator Research Articles

Multi-Contact Cartesian Null-Space Impedance Control for the Anthropomorphic Manipulator Without Knowledge of Force Locations

Multi-Contact Cartesian Null-Space Impedance Control for the Anthropomorphic Manipulator Without Knowledge of Force Locations

Learning from demonstration for 7-DOF anthropomorphic manipulators without offset via analytical inverse kinematics

Learning from demonstration (LfD) has been widely studied as a convenient method for robot learning. In the LfD paradigm for redundant manipulators, the reproduced trajectories ought to be similar to human demonstrations in both task and joint space. Despite many advancements achieved in the context of learning in task space, the solutions for generating similar reproductions in joint space are still largely open. In this paper, a novel non-parametric LfD framework for 7-DOF anthropomorphic manipulators with high computational efficiency is proposed. The proposed method leverages redundancy resolution and kernel-based approaches to formulate an efficient model characterized by a limited set of open parameters. Experiments were conducted to evaluate the performance of the proposed method and compare it with the commonly used ‘LfD+IK’ solution. The results indicated that the proposed method behaves much better in terms of the similarity between the demonstration and reproduction with high computing efficiency. As the proposed method can learn from human demonstrations effectively in both task and joint space, it has the potential to significantly enhance human–robot collaboration, streamline assembly line processes, or improving robot learning. An important future challenge will be extending the proposed method for general-purpose redundant manipulators and considering task constraints to perform complex tasks.

Adjustable backstepping fuzzy controller for a 7 DOF anthropomorphic manipulator

Adjustable backstepping fuzzy controller for a 7 DOF anthropomorphic manipulator

An Analytical Inverse Kinematics Solution with the Avoidance of Joint Limits, Singularity and the Simulation of 7-DOF Anthropomorphic Manipulators

An Analytical Inverse Kinematics Solution with the Avoidance of Joint Limits, Singularity and the Simulation of 7-DOF Anthropomorphic Manipulators

РАЗРАБОТКА МОДЕЛИ КИНЕМАТИКИ ПАЛЬЦЕВ ЧЕЛОВЕЧЕСКОЙ РУКИ, ПРИМЕНЯЕМЫХ В РОБОТИЗИРОВАННЫХ ПЕРЧАТКАХ

This paper considers the design of an anthropomorphic manipulator in the form of a human hand. The aim of the work is to describe the kinematics of a robotic hand, using the example of one finger. Based on the Denavit–Hartenberg algorithm, a kinematic model is developed for one finger of the robotic hand. The working area of the robotic finger is determined, taking into account the limitations on the rotation angles of the finger phalanges. These kinematic equations are used to solve the problem of the speeds of one fingertip. According to the results of modeling for given speeds of the fingertip, the dependencies of the rotation angles of the robotic hand links were obtained. Unlike the previously used models, the developed one makes it possible to take into account the presence of a geometric constraint between the proximal and distal finger phalanges, which is structurally made with an inextensible thread. The results of the work can be used in the design and manufacture of new robotic gloves.

Error modeling and analysis of a spherical parallel mechanism with a multiloop circuit incremental method

A spherical parallel mechanism plays an important role in the shoulder joint of an anthropomorphic manipulator. In this study, the error model of a 3-RRR+UR (R is a revolute pair, U is a universal joint) spherical parallel mechanism is established, the error sensitivity is analyzed, and a design structure is proposed to reduce the input errors. The error modeling process includes more input error parameters, which are based on the multiloop circuit incremental method. Compared with the traditional loop circuit incremental method, the multiloop circuit incremental method can be used to calculate more than one loop. The input errors in the error model include manufacturing tolerance, assembly clearance, clearance of moving pairs, transmission structure clearance and motor errors. An error sensitivity analysis is carried out to investigate the effects of these input error parameters on the spherical parallel mechanism. For parameters with high sensitivity, the influences of two kinds of input drives on them are compared, and the best configuration is selected. The error model is simulated and verified by experiments and compared with the vector method. The experimental results show that the result of the multiloop circuit incremental method is more consistent with the actual value. This paper proposes a concise, intuitive and precise error modeling method for multiloop spatial spherical parallel mechanisms.

Global Redundancy Optimization of Manipulability for 7-DOFs Anthropomorphic Manipulator Using Joint Monotonicity

Global Redundancy Optimization of Manipulability for 7-DOFs Anthropomorphic Manipulator Using Joint Monotonicity

A Wearable Upper Limb Exoskeleton for Intuitive Teleoperation of Anthropomorphic Manipulators

Teleoperation technology combines the strength and accuracy of robots with the perception and cognition abilities of human experts, allowing the robots to work as an avatar of the operator in dangerous environments. The motion compatibility and intuitiveness of the human–machine interface directly affect the quality of teleoperation. However, many motion capture methods require special working environments or need bulky mechanisms. In this research, we proposed a wearable, lightweight, and passive upper limb exoskeleton, which takes intuitiveness and human-machine compatibility as a major concern. The upper limb pose estimation and teleoperation mapping control methods based on the exoskeleton are also discussed. Experimental results showed that by the help of the upper limb exoskeleton, people can achieve most areas of the normal range of motion. The proposed mapping control methods were verified on a 14-DOF anthropomorphic manipulator and showed good performance in teleoperation tasks.

Kinematics optimization of a novel 7-DOF redundant manipulator

Redundant manipulators can accomplish complex tasks due to the redundant degree of freedom (DOF). At the same time, their kinematics calculations are complicated. In this study, the kinematics solution for a self-designed 7-DOF redundant anthropomorphic manipulator is obtained, and an optimization method is provided to optimize the motion time of the manipulator in the same trajectory. Kinematics analysis of the spherical parallel shoulder joint was performed by using a geometric method and coordinate transformation. Kinematics analysis of the redundant manipulator was performed by a geometric method with an optimizing arm angle. Linearly decreasing weight particle swarm optimization (LDWPSO) was used to optimize the arm angle to minimize the motion time of the manipulator. The kinematics calculation of the shoulder joint was verified by a combination of SOLIDWORKSTM and MATLABTM software. The kinematics calculation of the redundant manipulator was verified by MATLABTM. The linear, circular and 8-shaped motion trajectories were used to evaluate the proposed method in the simulation. The simulation results showed that the motion time with optimization of the arm angle was only 16%–30% of that without optimization. Furthermore, the proposed method was evaluated through real manipulator experiments, and the experimental results were similar to those in the simulation.

Deep Instance Segmentation and Visual Servoing to Play Jenga with a Cost-Effective Robotic System.

The game of Jenga is a benchmark used for developing innovative manipulation solutions for complex tasks. Indeed, it encourages the study of novel robotics methods to successfully extract blocks from a tower. A Jenga game involves many traits of complex industrial and surgical manipulation tasks, requiring a multi-step strategy, the combination of visual and tactile data, and the highly precise motion of a robotic arm to perform a single block extraction. In this work, we propose a novel, cost-effective architecture for playing Jenga with e.Do, a 6DOF anthropomorphic manipulator manufactured by Comau, a standard depth camera, and an inexpensive monodirectional force sensor. Our solution focuses on a visual-based control strategy to accurately align the end-effector with the desired block, enabling block extraction by pushing. To this aim, we trained an instance segmentation deep learning model on a synthetic custom dataset to segment each piece of the Jenga tower, allowing for visual tracking of the desired block's pose during the motion of the manipulator. We integrated the visual-based strategy with a 1D force sensor to detect whether the block could be safely removed by identifying a force threshold value. Our experimentation shows that our low-cost solution allows e.DO to precisely reach removable blocks and perform up to 14 consecutive extractions in a row.

A Robust Visual Servoing Controller for Anthropomorphic Manipulators With Field-of-View Constraints and Swivel-Angle Motion: Overcoming System Uncertainty and Improving Control Performance

Human–robot collaboration has attracted significant attention in the industry due to the flexibility of humans and the accuracy of robots. Humanoid control of anthropomorphic robotic arms combined with visual servoing will enhance the intelligence of industrial robots. However, the robotic manipulator will introduce psychological discomfort to nearby humans, and the loss of visual features will induce visual servoing task failure. Aiming at these problems, this article proposes a humanoid control method based on visual servoing by utilizing the swivel angle derived from the human arm to realize the human-like behavior of anthropomorphic robot manipulators. To advance the visual servoing control performance, a function constraint is designed with the barrier Lyapunov function (BLF) to ensure that image features stay within the field of view (FoV). The sliding mode control (SMC) is combined with image-based visual servoing (IBVS) to dispose of the uncertainties of a seven-degree-of-freedom (7-DoF) redundant robot manipulator. The proposed algorithm is substantiated through comparison experiments based on the Sawyer robot and constructed visual servoing physical platform.

Human-like redundancy resolution: An integrated inverse kinematics scheme for anthropomorphic manipulators with radial elbow offset

As a mimic of the human arm structure, anthropomorphic manipulators with radial elbow offset (AMREO) are often deployed on humanoid service robots. However, the unique offset leads to difficulties in solving the analytical inverse kinematics (IK), which poses a challenge for further anthropomorphic control. This paper presents an integrated scheme for solving the path-wise IK problem of a 7-DoF AMREO in the position domain. Unlike other approaches, special attention is paid to the naturalness of the arm configuration, with the aim of making AMREO exhibit human-like behavior in human-centered environments. First, an analytical IK solution of AMREO for a single end-effector pose is derived based on the arm angle parameterization. Then, inspired by the habitual arm configurations in human reaching movements, the natural arm configuration mapped to wrist position is proposed for AMREO. To learn the patterns implied therein, a LSTM-based natural arm angle prediction network (NAPN) is designed and trained based on a human demonstration dataset. Finally, a redundancy resolution framework embedded with NAPN is built to generate smooth and natural joint configurations in the path-wise IK tasks. Comparative experiments show that the proposed analytical IK algorithm has better computational efficiency and precision than conventional methods, and can give complete results for one IK call within 4 μs. In addition, continuous path tracking experiments on a real robot validate the effectiveness and anthropomorphism of the redundancy resolution scheme based on NAPN.

Modeling and simulation of two ambidextrous anthropomorphic manipulators to perform cooperative tasks

This paper is focusing on the development of a robotic arm using 3D modeling and simulation. The software used is Blender, which is a 3D modeling and simulation software that also supports programming language python as the scripting language. Blender was chosen over Maya because it is a free software suitable for students to develop their projects, and share the same features, more accessible, and the design is more realistic. The robotic arm was designed after studying the human arm and hand. The kinematics of the robotic arm were derived. The simulation shows the movement of rigged objects, e.g., an arm controlling a gun, and bullets projecting from the gun, and it was done using key frame animation and game engine simulation. Modeling and simulation of two robotic arms and hands, shooting with an AK-47 rifle at a bullseye, were done and completed using python in Blender. This work is intended to be integrated into a First-Person Shooter FPS game, which can be used to train biathlon sportsmen and army soldiers for precise shooting. Forward and inverse kinematics are implemented for the rig to move without breaking and deforming, then the bones and mesh together are combined as one unit, then the joined mesh is put to a desired position of a shooting position, then a rifle is added, then the bullets are simulated falling on ground when the bullets hits the target. Then finally the whole file is exported.

Neural Adaptive Robust Motion-Tracking Control for Robotic Manipulator Systems

This paper deals with the motion trajectory tracking control problem based on output feedback and artificial neural networks for anthropomorphic manipulator robots under disturbed operating scenarios. This class of manipulator robots constitutes nonlinear dynamic systems subjected to disturbance torques induced mainly by work payload. Parametric uncertainty and possible dynamic modeling errors stand for other kind of disturbances that can deteriorate the efficiency and robustness of the tracking of controlled nonlinear robotic system trajectories. In fact, the presence of unknown dynamic disturbances is unavoidable in industrial robotic engineering systems. Therefore, for high-precision applications, such as laser cutting, marking, or welding, effective control schemes should be designed to guarantee adequate motion profile tracking planned on this class of disturbed nonlinear robotic system. In this context, a new adaptive robust motion trajectory tracking control scheme based on output feedback and artificial neural networks of anthropomorphic manipulator robots is presented. Three-layer B-spline artificial neural networks and time-series modeling are properly exploited in the design of novel adaptive robust motion tracking controllers for robotic applications of laser manufacturing. In this way, dependency on detailed nonlinear mathematical modeling of robotic systems is considerably reduced, and real-time estimation of uncertain dynamic disturbances is not required. Furthermore, several cases studies to demonstrate the motion planning tracking control robustness for a class of MIMO nonlinear robotic systems are described. blue Insights for the extension of the introduced output-feedback adaptive neural control design approach for other architecture of nonlinear robotic systems are depicted.

Pattern Recognition of EMG Signals by Machine Learning for the Control of a Manipulator Robot

Human Machine Interfaces (HMI) principles are for the development of interfaces for assistance or support systems in physiotherapy or rehabilitation processes. One of the main problems is the degree of customization when applying some rehabilitation therapy or when adapting an assistance system to the individual characteristics of the users. To solve this inconvenience, it is proposed to implement a database of surface Electromyography (sEMG) of a channel in healthy individuals for pattern recognition through Neural Networks of contraction in the muscular region of the biceps brachii. Each movement is labeled using the One-Hot Encoding technique, which activates a state machine to control the position of an anthropomorphic manipulator robot and validate the response time of the designed HMI. Preliminary results show that the learning curve decreases when customizing the interface. The developed system uses muscle contraction to direct the position of the end effector of a virtual robot. The classification of Electromyography (EMG) signals is obtained to generate trajectories in real time by designing a test platform in LabVIEW.

Research on Speed and Acceleration of Hand Movements as Command Signals for Anthropomorphic Manipulators as a Master-Slave System

Due to threats to human safety, remotely controlled manipulators are more and more often used to carry out rescue tasks in hazardous zones. To ensure high efficiency and productivity of their work, intuitive control systems are necessary, e.g., master-slave and drive systems that maximize the speed of working movements by copying the movements of the operator’s hands and are adapted to human perception and capabilities. Proper design of manipulator drive and control systems, therefore, requires knowledge of the acceleration and velocity of hand movements as signals controlling manipulators. This paper presents the results of tests of speed and acceleration in the implementation of the hand when making precise movements and moving objects over short distances (0.4–0.5 m) and during relatively long-distance reaching movements (0.73–0.93 m). Research has shown that, at short distances, the hand movements do not reach the maximum speed, while at longer distances, there is a period of constant maximal speed. In addition, studies have shown that the maximum speed of manipulation movements (longitudinal, lateral, and vertical) does not depend on the direction of movement. Moreover, precise movements were performed at a much slower velocity than reaching movements.

Modified intelligent bidirectional random tree algorithm for planning the movement of anthropomorphic manipulators

Modified intelligent bidirectional random tree algorithm for planning the movement of anthropomorphic manipulators

An Artificial Neural Network Approach for Solving Inverse Kinematics Problem for an Anthropomorphic Manipulator of Robot SAR-401

The paper proposes a new design of an artificial neural network for solving the inverse kinematics problem of the anthropomorphic manipulator of robot SAR-401. To build a neural network (NN), two sets were used as input data: generalized coordinates of the manipulator and elements of a homogeneous transformation matrix obtained by solving a direct kinematics problem based on the Denavi–Hartenberg notation. According to the simulation results, the NN based on the homogeneous transformation matrix showed the best accuracy. However, the accuracy was still insufficient. To increase the accuracy, a new NN design was proposed. It consists of adding a so-called “correctional” NN, the input of which is fed the same elements of the homogeneous transformation matrix and additionally the output of the first NN. The proposed design based on the correctional NN allowed the accuracy to increase two times. The application of the developed NN approach was carried out on a computer model of the manipulator in MATLAB, on the SAR-401 robot simulator, as well as on the robot itself.

Adaptive FIT-SMC Approach for an Anthropomorphic Manipulator With Robust Exact Differentiator and Neural Network-Based Friction Compensation

In robotic manipulators, feedback control of nonlinear systems with fast finite-time convergence is desirable. However, because of the parametric and model uncertainties, the robust control and tuning of the robotic manipulators pose many challenges related to the trajectory tracking of the robotic system. This research proposes a state-of-the-art control algorithm, which is the combination of fast integral terminal sliding mode control (FIT-SMC), robust exact differentiator (RED) observer, and feedforward neural network (FFNN) based estimator. Firstly, the dynamic model of the robotic manipulator is established for the n-degrees of freedom (DoFs) system by taking into account the dynamic LuGre friction model. Then, a FIT-SMC with friction compensation-based nonlinear control has been proposed for the robotic manipulator. In addition, a RED observer is developed to get the estimates of robotic manipulator joints’ velocities. Since the dynamic friction state of the LuGre friction model is unmeasurable, FFNN is established for training and estimating the friction torque. The Lyapunov method is presented to demonstrate the finite-time sliding mode enforcement and state convergence for a robotic manipulator. The proposed control approach has been simulated in the MATLAB/Simulink environment and compared with the system with no observer to characterize the control performance. Simulation results obtained with the proposed control strategy affirm its effectiveness for a multi-DoF robotic system with model-based friction compensation having an overshoot and a settling time less than 1.5% and 0.2950 seconds, respectively, for all the joints of the robotic manipulator.

SOFTWARE METHOD FOR ANALYSIS OF THE PERFORMANCE OF ANTHROPOMORPHIC MANIPULATOR

A universal anthropomorphic manipulator with six rotational degrees of mobility is considered. The nodal points S0, ..., S6 are selected on the trajectory of movement of the manipulator grip. The kinematic analysis of the manipulator was carried out by the method of transforming the coordinates of Denavit - Hartenberg. The mathematical model of the manipulator is compiled by the Lagrange-Euler method. The problem of maximum performance for each generalized coordinate qi (t) is solved using the Pontryagin maximum principle. Mathematical modeling was carried out in the Mathcad environment. The software method for analyzing the speed of the manipulator allows us to consider for each nodal point of the trajectory S0, ..., S6 the set of admissible configurations of the manipulator, solve the problem of maximum speed for each generalized coordinate qi (t) and estimate the minimum time for the implementation of the sequence of state vectors. When solving the problem of maximum speed, the switching time ti1 and the minimum turning time tik are calculated for each generalized coordinate qi (t). The minimum time for the configuration implementation can be found by summing the minimum rotation time tik over six generalized coordinates. Similar calculations are carried out for each nodal point S0, ..., S6 and the corresponding sets of permissible manipulator configurations. The developed software makes it possible, on the basis of the data obtained, to synthesize a sequence of control commands for the manipulator drives. The research results can be used at the design stage, implementation and modernization of robotic systems and manipulators.