Motion Of Manipulator Research Articles

Bioinspired Soft Wrist Based on Multicable Jamming With Hybrid Motion and Stiffness Control for Dexterous Manipulation

Dexterous manipulation is important for modern logistics automation. Industrial robots remain inferior to human labor in this area, especially in handling unknown objects in unstructured scenarios. It is observed that the human wrist contributes significantly to the dexterous manipulation capability by agilely changing the hand orientation to approach the target object and adjusting wrist stiffness to adapt to dynamic interaction. Inspired by the human wrist, we propose a novel soft wrist to achieve hybrid motion/stiffness control in a compact and lightweight structure. The dexterous motion and stiffness adjustment are simultaneously enabled by a novel multicable jamming mechanism, which is achieved by a precalibration of the relationship between stiffness with robot length and bending angle to adapt to both compliant and forceful tasks. Dedicated experiments were performed to validate its motion dexterity and variable stiffness property. A soft grasping system was developed to demonstrate the manipulation capability of the soft wrist by tasking it with automatic packaging, which is challenging for robots. Overall, the proposed soft wrist demonstrates a promising solution to enhance robotic manipulation capability to a human comparable level.

Simulation, Learning, and Application of Vision-Based Tactile Sensing at Large Scale

Large-scale robotic skin with tactile sensing ability is emerging with the potential for use in close-contact human–robot systems. Although recent developments in vision-based tactile sensing and related learning methods are promising, they have been mostly designed for small-scale use, such as by fingers and hands, in manipulation tasks. Moreover, learning perception for such tactile devices demands a huge tactile dataset, which complicates the data collection process. To address this, this study introduces a multiphysics simulation pipeline, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SimTacLS</i> , which considers not only the mechanical properties of external physical contact but also the realistic rendering of tactile images in a simulation environment. The system utilizes the obtained simulation dataset, including virtual images and skin deformation, to train a tactile deep neural network to extract high-level tactile information. Moreover, we adopt a generative network to minimize sim2real inaccuracy, preserving the simulation-based tactile sensing performance. Last but not least, we showcase this sim2real sensing method for our large-scale tactile sensor ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TacLink</i> ) by demonstrating its use in two trial cases, namely, whole-arm nonprehensile manipulation and intuitive motion guidance, using a custom-built tactile robot arm integrated with TacLink. This article opens new possibilities in the learning of transferable tactile-driven robotics tasks from virtual worlds to actual scenarios without compromising accuracy.

Online motion planning based on swept volume search with replanning using sequential quadratic programming

This paper describes a method for obtaining a mobile manipulator's motion sequence by indicating a goal-hand pose. The proposed method entails recording various robot motions as a large number of motion sequences and associated swept volumes (SVs) and then selecting the most appropriate SV for the current situation. In addition, the motion sequences can be altered while the robot is in motion, and the transition motion is generated using sequential quadratic programming, enabling the robot to avoid collisions with obstacles found after it has begun to move. The proposed method's performance is verified by simulation to confirm the appropriate amount of data and the method's superiority.

Learning Continuous Grasping Function With a Dexterous Hand From Human Demonstrations

We propose to learn to generate grasping motion for manipulation with a dexterous hand using implicit functions. With continuous time inputs, the model can generate a continuous and smooth grasping plan. We name the proposed model Continuous Grasping Function (CGF). CGF is learned via generative modeling with a Conditional Variational Autoencoder using 3D human demonstrations. We will first convert the large-scale human-object interaction trajectories to robot demonstrations via motion retargeting, and then use these demonstrations to train CGF. During inference, we perform sampling with CGF to generate different grasping plans in the simulator and select the successful ones to transfer to the real robot. By training on diverse human data, our CGF allows generalization to manipulate multiple objects. Compared to previous planning algorithms, CGF is more efficient and achieves significant improvement on success rate when transferred to grasping with the real Allegro Hand. Our project page is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://jianglongye.com/cgf/</uri> .

Analytical and deep learning approaches for solving the inverse kinematic problem of a high degrees of freedom robotic arm

Inverse kinematics is the basis for controlling the motion of robotic manipulators. It defines the required joint variables for the robotic end-effector accurately reach the desired location. Due to the derivation difficulty, computation complexity, singularity problem, and redundancy, analytical Inverse kinematics solutions pose numerous challenges to the operation of many robotic arms, especially for a manipulator with a high degree of freedom. This paper develops different Deep Learning networks for solving the Inverse kinematics problem of six- Degrees of Freedom robotic manipulators. The implemented neural architectures are Artificial Neural Network, Convolutional Neural Network, Long–Short Term Memory, Gated Recurrent Unit, and Bidirectional Long–Short Term Memory. In this context, we associate the proposed results with a specific tuning of Deep Learning network hyper-parameters (number of hidden layers, learning rate, Loss function, optimization algorithm, number of epochs, etc.). The Bidirectional Long–Short Term Memory network outperformed all proposed architectures. To be close as possible to the experimental results, we have included two types of noise in the training data set to validate which of the five proposed neural networks is more efficient. Furthermore, in this study, we compare the performance of analytical and soft computing solutions in generating robots’ trajectories. We include this scenario, focusing on the advantage of implementing neural networks in avoiding the singularity problem that can occur using the analytical approach. In addition, we used the RoboDK simulator to show simulation results with real-world meaning. The performance of Deep Learning models depends on the complexity of the posed problem. Moreover, the complexity of the Inverse Kinematics problem is related to the number of Degrees of Freedom. At the end of this work, we evaluate the influence of the complexity of robotic manipulators on the proposed Deep Learning networks’ performance. The results show that the implemented Deep Learning mechanisms performed well in reaching the desired pose of the end-effector. The proposed inverse kinematics strategies apply to other manipulators with different numbers of Degrees of Freedom.

Sensor Fusion-Based Teleoperation Control of Anthropomorphic Robotic Arm

Sensor fusion is a technique that combines information from multiple sensors in order to improve the accuracy and reliability of the data being collected. In the context of teleoperation control of an anthropomorphic robotic arm, sensor fusion technology can be used to enhance the precise control of anthropomorphic robotic arms by combining data from multiple sensors, such as cameras, data gloves, force sensors, etc. By fusing and processing this sensing information, it can enable real-time control of anthropomorphic robotic arms and dexterous hands, replicating the motion of human manipulators. In this paper, we present a sensor fusion-based teleoperation control system for the anthropomorphic robotic arm and dexterous hand, which utilizes a filter to fuse data from multiple sensors in real-time. As such, the real-time perceived human arms motion posture information is analyzed and processed, and wireless communication is used to intelligently and flexibly control the anthropomorphic robotic arm and dexterous hand. Finally, the user is able to manage the anthropomorphic operation function in a stable and reliable manner. We also discussed the implementation and experimental evaluation of the system, showing that it is able to achieve improved performance and stability compared to traditional teleoperation control methods.

Research on Configuration Design Optimization and Trajectory Planning of Manipulators for Precision Machining and Inspection of Large-Curvature and Large-Area Curved Surfaces.

In recent years, high-quality surfaces with large areas and curvatures have been increasingly used in engineering, but the precision machining and inspection of such surfaces is a particular challenge. Surface machining equipment needs to have a large working space, high flexibility, and motion accuracy to meet the demands of micron-scale precision machining. However, meeting these requirements may result in extremely large equipment sizes. To solve this problem, an eight-degree-of-freedom redundant manipulator with one linear and seven rotational joints is designed to assist in the machining described in this paper. The configuration parameters of the manipulator are optimized by an improved multi-objective particle swarm optimization algorithm to ensure that the working space of the manipulator completely covers the working surface and that the size of the manipulator is small. In order to improve the smoothness and accuracy of manipulator motion on large surface areas, an improved trajectory planning strategy for a redundant manipulator is proposed. The idea of the improved strategy is to pre-process the motion path first and then use a combination of the clamping weighted least-norm method and the gradient projection method to plan the trajectory, while adding a reverse planning step to solve the singularity problem. The resulting trajectories are smoother than those planned by the general method. The feasibility and practicality of the trajectory planning strategy are verified through simulation.

Motion Trajectory Error of Robotic Arm Based on Neural Network Algorithm

In order to solve the problems of unstable motion and large trajectory tracking error of the manipulator when it is disturbed by the outside world, the author proposes an adaptive neural network manipulator motion trajectory error method. The author gives the dynamic equation of the manipulator and uses the positive feedback neural network to study the dynamic characteristics of the manipulator. An adaptive neural network control system is designed, and the stability and convergence of the closed-loop system are proved by the Lyapunov function. A schematic diagram of the manipulator model is established, and MATLAB/Simulink software is used to simulate the dynamic parameters of the manipulator. At the same time, it is compared and analyzed with the simulation results of the PID control system. Simulation results show that in robot arm 3, the expected motion trajectory is θ3 = 0.4cos(2πt), the initial condition θ(0) = [000]τ, the control parameter K = diag(40,40),40), the disturbance parameter τ’ = 20cos(πt), robot arm link parameters l1 = 0.62 m, l2 = 0.41 m, l3 = 0.34 m, m1 = 3.5, m2 = 2.5 kg, m3 = 2 kg, g = 9.82 m/s2, under t = 2s, the motion trajectory of the robotic arm is disturbed by the outside world, and the adaptive neural network is used to control the motion trajectory with a small tracking error, input torque ripple is small. Conclusion. The manipulator adopts the adaptive neural network control method, which can improve the control accuracy of the motion trajectory and weaken the jitter phenomenon of the manipulator motion.

GEOMETRIC SIMULATION OF THE MOVEMENT PROCESS OF THE EXCAVATOR’S MILLING WORKING EQUIPMENT

The simulation of the process of movement of the milling working equipment of an excavator when digging a pipeline is discussed. The parameters of the repair and construction strip are calculated, the position of the coordinate systems of the excavator manipulator mechanism is established. A hypothesis has been put forward that the position of the excavator on the strip affects the volume of movement during digging, which increases energy consumption and reduces economic efficiency. The excavator manipulator is considered as a five-link mechanism. A kinematic scheme of the mechanism is given, on the basis of which a program based on the synthesis of movements along the velocity vector is created. The construction of the movement trajectory was carried out in such a way, that the output link was in a horizontal position throughout the entire movement process. The program was created using the Visual Lisp programming language built into the AutoCAD environment. The program made it possible to obtain graphic models of the movement of the excavator manipulator mechanism in its various positions, as well as to calculate the amount of movement. Based on the data obtained, a drawing by Radishchev was constructed, which reflected the dependence of the generalized coordinates of the excavator manipulator mechanism and the amount of movement on the depth of lowering of the working equipment. As a result, it was concluded that with the middle position of the excavator on the traffic lane, a smaller amount of traffic is provided, which means less energy consumption and a higher speed of work. The results of the conducted research can be used in the design of software and hardware systems for controlling moving objects, in particular, control systems for the motion of an excavator manipulator.

A Dynamic Multiple-Query RRT Planning Algorithm for Manipulator Obstacle Avoidance

Manipulator motion planning for real-time obstacle avoidance in a dynamic environment is explored in this article. To address obstacle avoidance problems, a multiple-query and sampling-based motion replanning algorithm with the dynamic bias-goal factor, rapidly exploring random tree (DBG-RRT), is proposed to achieve a rapid response and a high success rate. Differently from other studies on path planning, a relay-node method is adopted on the basis of motion planning to generate a new collision-free trajectory. Subsequently, an un-interrupt strategy is embraced to judge whether the generated trajectory would be interfered with by dynamic obstacles. In the end, the DBG-RRT algorithm is applied, and the results demonstrate its effectiveness for manipulator motion planning in a dynamic environment.

Mathematical modeling and simulation of the inverse kinematic of a redundant robotic manipulator using azimuthal angles and spherical polar piecewise interpolation

In this study, smooth trajectory generation and the mathematical modeling of the inverse kinematics and dynamics of a redundant robotic manipulator is considered. The path of the end-effector of the robotic manipulator is generated – along a particular set of via-points – in a system of spherical coordinates by combining the Hermite polar piecewise interpolants that approximate each intermediate radial distance on the reference plane using the azimuthal angle and each corresponding point height of the path computed in the rotating radial plane through the corresponding polar angle. The smoothness of the end-effector path is guaranteed by the use of quintic Hermite piecewise interpolants having functional continuity, while the inverse kinematics and inverse dynamics used to compute the coordinates of the joints and the equations of motion of the robotic manipulator are considered for optimum trajectory planning. Optimization solutions to minimize the joint velocities, the end-effector positioning error, the traveling time and mechanical energy, or the consumed power, are presented. The direct computation of the joints trajectory and a optimal trajectory with minimization of the end-effector position error while preserving trajectory smoothness of the joints are considered. To assess and verify the approach numerical examples – implemented in Matlab – are introduced, and the outcomes are examined.

Manipulator Motion Planning Based on Improved RRT Algorithm

Aiming at the problems that the Rapidly-exploring Random Tree (RRT) algorithm has more time-consuming and longer paths in the manipulator motion planning, we propose an improved RRT algorithm based on probabilistic target bias strategy and triangular inequality pruning method. This paper simulates the algorithm from three dynamic environments with different numbers of obstacles. The simulation results show that the proposed improved RRT algorithm can plan a shorter path in less time.

Flexible Needle Bending Model for Spinal Injection Procedures.

An in situ needle manipulation technique used by physicians when performing spinal injections is modeled to study its effect on needle shape and needle tip position. A mechanics-based model is proposed and solved using finite element method. A test setup is presented to mimic the needle manipulation motion. Tissue phantoms made from plastisol as well as porcine skeletal muscle samples are used to evaluate the model accuracy against medical images. The effect of different compression models as well as model parameters on model accuracy is studied, and the effect of needle-tissue interaction on the needle remote center of motion is examined. With the correct combination of compression model and model parameters, the model simulation is able to predict needle tip position within submillimeter accuracy.

Null-Space Minimization of Center of Gravity Displacementof a Redundant Aerial Manipulator

Displacements of the base during trajectory tracking are a common issue in the control of aerial manipulators. These are caused by reaction torques transferred to the base due to the manipulator motion and, in particular, to the motion of its center of gravity. We present a novel approach to reduce base displacements of a kinematically redundant aerial manipulator by using null-space projection in the inverse kinematic control. A secondary objective function minimizes the horizontal displacement of the manipulator center of gravity. We test this algorithm on different trajectories for both three and four degrees of freedom (DOF) manipulators in a simulation environment. The results comparing our algorithm with inverse kinematic control without the null-space projection show up to an 80% reduction in the end-effector position error and an average of about 56% reduction in maximum base displacement. The simulation implementation also runs faster than in real-time in our code implementation. We provide a workspace analysis based on multiple stopping criteria such as excessive base displacement, joint velocities and end-effector position error for the 3 and 4 DOF manipulators. As expected, the 4 DOF manipulator has a larger workspace.

A Novel Walking Parallel Robot for On-Structure Three-Axis Machining of Large Structures

Abstract This paper presents a novel walking hybrid-kinematics robot having three degrees-of-freedom for on-structure machining of large structures. A symmetric 3PRRR parallel mechanism having maximally regular properties is used to provide three-dimensional translational manipulation. Three attachment pads are connected to the base of the parallel mechanism through passive spherical joints, whereas multiple attachment pads are connected to the moving platform of the parallel mechanism. A machining task is performed by using a retractable tool holder attached to the parallel mechanism's moving platform. Two walking patterns, namely rotational and translational walking patterns, are defined for the robot. The kinematics of the manipulation and walking motions was derived and simulated. Several schemes to perform multi-step walking motions were also discussed. Subsequently, using an energy-based approach with the Stribeck friction model, the robot's dynamics was modeled and experimentally verified. Finally, an implementation of the robot to perform an on-structure machining task is discussed.

Trajectory planning algorithm of manipulator based on NURBS

This paper uses the 5Dof manipulator on the YouBot developed by KUKA as a model. Combining quintic polynomial interpolation with quintic non-uniform rational B splines, a novel segmented polynomial interpolation planning technique 5-B-5 is proposed in this study. CoppeliaSim constructs the model, and MATLAB controls the model to acquire the angular displacement, angular velocity, angular acceleration, final trajectory, and joint force for every joint angle. The new optimization algorithm presented in this work is utilized to optimize the manipulator's motion trajectory. Comparing it to the classic 3-5-3 algorithm and the B spline algorithm offers advantages over the higher-order B spline interpolation without increasing the manipulator control system's operational overhead. The experiments prove that the motion trajectory inaccuracy of the manipulator is minimized, the curve is continuous and smooth, and the smoothness is improved, thereby eliminating mechanical shock and other issues.

Active learning of time-optimal motion for an industrial manipulator with variable payload

Generating optimal motion for robotic systems remains an active field of research, despite its relatively long history. In addition to being optimal with respect to a defined cost, such a motion must respect the electro-mechanical limitations of the system to be of use in real-world applications. Furthermore, in an industrial application where offline planning is not possible due to a priori unknown targets and payloads, the available time budget for computations is often highly limited, making many optimal motion planning approaches unsuitable. This work introduces a supervised learning-based motion planner for industrial manipulators that closely retains the superior performance of an optimal motion planner while requiring a fraction of the computations at runtime. The proposed motion planner implicitly considers the dynamics of the robot and therefore satisfies not only kinematic limits but also those of the actuators. The proposed method is compared to state-of-the-art approaches and demonstrated on a real robot.

Геометрическое моделирование процессов выполнения подкопочных работ экскаватором с целью предотвращения повреждения стенки трубопровода

When controlling the motion of the excavator bucket, the operator controls the translational motions in the hydraulic cylinders, which are generally independent. However, ensuring the motion of the cutting edge of the bucket along a given trajectory (for example, along a horizontal straight line) requires certain skills and additional concentration of attention from the operator. This significantly affects the fatigue of the operator during these works. In this case, it becomes relevant to solve the problem of developing an automated control system that makes it possible to calculate the change in the angles of rotation in the rotary joints of the excavator, which provide the specified motion of the bucket. In this case, the operator can only indicate the start and target points of the trajectory and the direction of motion of the bucket. In the article, based on the research, an algorithm has been developed that allows you to simulate the motion of points of an excavator bucket with a given distance from the upper edge of the pipeline. The results of computer simulation of the motion of the excavator manipulator are presented.

A Novel Predefined Time PD-Type ILC Paradigm for Nonlinear Systems

Intelligent robotics has drawn a great deal of attention due to its high precision, stability, and reliability, which are the basic key factors for industrial automation. This paper proposes an iterative learning control (ILC) technique with predefined-time convergence as a solution to an applied engineering problem, namely, that local time cannot be preset when a second-order nonlinear system undertakes control of the accurate tracking of local time under any initial iterative value. A time-varying sliding surface with an initial value of zero was designed, and it was theoretically proven that the trajectory tracking error in the sliding surface could converge to zero within a predefined time. The iterative control problem of trajectory tracking was thus changed to an iterative control problem of time-varying sliding-mode surface tracing with a starting value of zero. A PD-type closed-loop ILC with a time-varying sliding mode surface was designed such that the trajectory tracking error converged and stabilized on the sliding mode surface after a finite number of learning iterations. The control goal for the system’s output was the ability to track the desired trajectory accurately within a predefined time interval, and it was achieved by combining this with the predefined time convergence characteristics of the time-varying sliding mode surface. Numerical simulation of trajectory tracking control of a repetitive motion manipulator was used to verify the effectiveness of the proposed controller and its robustness in the face of external disturbances.

The effect of losartan on range of motion and rates of manipulation in total knee arthroplasty: a retrospective matched cohort study.

Arthrofibrosis remains a common cause of patient dissatisfaction and reoperation after total knee arthroplasty (TKA). Losartan is an angiotensin receptor blocker (ARB) with inhibitory effects on transforming growth factor beta, previously implicated in tissue repair induced fibrosis, and has been studied to prevent stiffness following hip arthroscopy. This study aimed to evaluate pre- and postoperative range of motion (ROM) and the incidence of manipulation under anesthesia (MUA) following primary TKA in patients taking Losartan preoperatively for hypertension. A retrospective review of 170 patients from 2012 to 2020 who underwent a primary, elective TKA and were prescribed Losartan at least three months prior to surgery. All patients who were prescribed Losartan and had a preoperative and postoperative ROM in their chart were included and were matched to a control group of patients who underwent TKA and had no Losartan prescription. ROM, MUA, readmissions, reoperations, and revisions were assessed using chi-square and independent sample t tests. Seventy-nine patients met the inclusion criteria. Preoperative ROM was similar between patients on Losartan and the control group (103.59° ± 16.14° vs. 104.59° ± 21.59°, respectively; p = 0.745). Postoperative ROM and ΔROM were greater for patients prescribed Losartan (114.29° ± 12.32° vs. 112.76° ± 11.65°; p = 0.429 and 10.57° ± 14.95° vs. 8.17° ± 21.68°; p = 0.422), though this difference did not reach statistical significance. There was no difference in readmission, rate of manipulation for stiffness, or all-cause revision rates. In this study, we found that the use of Losartan did not significantly improve postoperative ROM, reduce MUA or decrease revision rates. Further prospective studies using Losartan are required to elucidate the potential effects on ROM and incidence of arthrofibrosis requiring MUA. Retrospective cohort study.