Inverse Kinematics Research Articles

Enhancing Telexistence Control Through Assistive Manipulation and Haptic Feedback

The COVID-19 pandemic brought telepresence systems into the spotlight, yet manually controlling remote robots often proves ineffective for handling complex manipulation tasks. To tackle this issue, we present a machine learning-based assistive manipulation approach. This method identifies target objects and computes an inverse kinematic solution for grasping them. The system integrates the generated solution with the user’s arm movements across varying inverse kinematic (IK) fusion levels. Given the importance of maintaining a sense of body ownership over the remote robot, we examine how haptic feedback and assistive functions influence ownership perception and task performance. Our findings indicate that incorporating assistance and haptic feedback significantly enhances the control of the robotic arm in telepresence environments, leading to improved precision and shorter task completion times. This research underscores the advantages of assistive manipulation techniques and haptic feedback in advancing telepresence technology.

Optimization of manipulator inverse kinematics using improved particle swarm algorithm for enhanced manufacturing efficiency

Optimization of manipulator inverse kinematics using improved particle swarm algorithm for enhanced manufacturing efficiency

Design and Performance Analysis of a Novel Group of Translational Parallel Robots for a Three-Axis Grinding Machine

There are limited parallel robots applicable to three-axis grinding machines due to the restricted reachable workspace originating from multiple kinematic chains with spatial kinematic joints. The parallel robot will gain significant potential in the industry if a larger workspace can be achieved. This research introduces a special relationship between upper triangular matrix and parallel robot structures for the purpose of designing a group of novel parallel robots without spatial kinematic joints. The detailed inverse kinematic solution of the selected parallel manipulator is derived in accordance with its straightforward architecture. Several singularity configurations are found on the basis of the first-order kinematic relation. The translational reachable workspace is close to a triangular prism. The dexterity and stiffness performances based on the Jacobian matrix are explored for the chosen parallel manipulator. Both indices display a downward trend as the mobile platform rises higher.

Machine learning enabled measurements of astrophysical ( p,n ) reactions with the SECAR recoil separator

The synthesis of heavy elements in supernovae is affected by low-energy (n,p) and (p,n) reactions on unstable nuclei, yet experimental data on such reaction rates are scarce. The SECAR (SEparator for CApture Reactions) recoil separator at FRIB (Facility for Rare Isotope Beams) was originally designed to measure astrophysical reactions that change the mass of a nucleus significantly. We used a novel approach that integrates machine learning with ion-optical simulations to find an ion-optical solution for the separator that enables the measurement of (p,n) reactions, despite the reaction leaving the mass of the nucleus nearly unchanged. A new measurement of the Fe58(p,n)Co58 reaction in inverse kinematics with a 3.66±0.12 MeV/nucleon Fe58 beam (corresponding to 3.69±0.12 MeV proton energy in normal kinematics) yielded a cross-section of 20.3±6.3 mb and served as a proof of principle experiment for the new technique demonstrating its effectiveness in achieving the required performance criteria. This novel approach paves the way for studying astrophysically important (p,n) reactions on unstable nuclei produced at FRIB. Published by the American Physical Society 2025

Optimal design of a generalized single-loop parallel manipulator with RCM characteristic considering motion/force transmissibility

Abstract In certain scenarios, the large footprint of a robot is not conducive to multi-robot cooperative operations. This paper presents a generalized single-loop parallel manipulator with remote center of motion (GSLPM-RCM), which addresses this issue by incorporating a reconfigurable base. The footprint of this RCM manipulator can be adjusted by varying the parameters of the reconfigurable base. First, utilizing configuration evolution, a reconfigurable base is constructed based on the principle of forming RCM motion. Then, according to the modular analysis method, the inverse kinematics of this parallel RCM manipulator is analyzed, and the workspace is also analyzed. Subsequently, the motion/force transmissibility of this RCM manipulator is analyzed by considering its single-loop and multi-degree of freedom characteristics. Leveraging the workspace index and transmissibility indices, dimension optimization of the manipulator is implemented. Finally, the influence of the reconfigurable base on the workspace and the transmissibility performance of the optimized manipulator is studied.

Steam Generator Maintenance Robot Design and Obstacle Avoidance Path Planning.

To solve the issue of inconvenient and dangerous manual operation during the installation and removal of the main pipe plugging plate in the steam generator in nuclear power plants, a ten-degree-of-freedom plugging robot was designed in the present study that includes a collaborative robotic arm coupled with a servo electric cylinder. By establishing a joint coordinate system for the robot model, a D-H parameter model for the plate plugging robot was established, and the forward and inverse kinematics were solved. The volume level approximate convex decomposition algorithm was used to fit the steam generator model with a convex packet, and an experimental simulation platform was constructed. Lastly, path planning was carried out by using the RRT algorithm, with the paths divided into three phases for analysis. The simulation results show that the path in the first stage is relatively smooth, and the parameter changes in each joint are relatively stable. The path in the second stage exhibits zigzagging, with the parameter change curve of joint 9 being particularly evident, and the path in the third stage still exhibits zigzagging-the parameter change curves of joints 3 and 10 are particularly evident. The results of the present study show that, although the paths show a certain degree of zigzagging in complex environments, the plate plugging robot is still able to automatically complete the plate plugging task while avoiding obstacles, which greatly reduces the risk posed to the operator when exposed to a high-radiation environment, in addition to having certain research and application value.

Towards a shape-performance integrated digital twin for lumbar spine analysis

Background With significant advancement and demand for digital transformation, the digital twin has been gaining increasing attention as it is capable of establishing real-time mapping between physical space and virtual space. In this work, a digital twin solution is presented to predict the real-time biomechanics of the lumbar spine during human movement. Methods A finite element model (FEM) of the lumbar spine was firstly developed using computed tomography (CT) and constrained by the body movement which calculated by the inverse kinematics algorithm. The Gaussian process regression was utilized to train the predicted results and create the digital twin of the lumbar spine in real-time. Finally, a three-dimensional virtual reality system was developed using Unity3D to display and record the real-time biomechanics performance of the lumbar spine during body movement. Results The evaluation results presented an agreement (R 2 >0.8) between the real-time prediction from digital twin and offline FEM prediction. Conclusions This approach provides an effective method of real-time planning and warning in spine rehabilitation.

SCORBOT arm control using pseudoinverse jacobian control for Bézier path

The kinematics of manipulators represents a fundamental challenge in the field of robotic automatic control. This challenge involves transitioning between Cartesian space and combinatorial space. The kinematic equations governing motion are formulated using the Denavit-Hartenberg (D-H) notation. This paper introduces a method for the inversion of a SCORBOT ER-V plus manipulator, aimed at meticulously analyzing the arm's movement from one spatial point to another. To implement this solution, it is necessary to identify only the starting and ending points in space, along with the geometric constraints of the manipulator. The SCORBOT-ER V plus is a straightforward top-bottom robot featuring five revolute joints, recognized for its reliability and safety, particularly in laboratory and training environments. This experiment provides students with practical experience in robotics, automation, and control systems, highlighting the principles related to the operation and causation of various phenomena. The Python programming language is utilized to develop a mathematical model that complies with a defined set of constraints and guidelines. The analysis of object motion demonstrated that the output from the Python script closely aligned with the actual measurements obtained from the robotic arm. Differential movement is employed as a technique to analyze and characterize motion at different points of the robot, thereby facilitating the examination of robotic movement over short durations. This paper discusses both forward modeling and inverse kinematics within the realm of robotics. The traditional D-H model is applied to create the mathematical framework essential for determining and assessing the position of the end-effector in a 5-Degree-Of-Freedom (5DOF) robot, which will then be used to calculate the necessary joint configurations. Finally, we present the results derived from this experiment.

Research on Inverse Kinematics and Block Grasping Techniques for Six-Axis Robotic Arms

Research on Inverse Kinematics and Block Grasping Techniques for Six-Axis Robotic Arms

Stroke phase differences in the linear relationship between swimming velocity and vertical body position during front crawl

ABSTRACT We aimed to investigate whether a linear relationship exists between swimming velocity and vertical body position for each stroke phase in front crawl, and to determine whether there are differences in the velocity effect among the stroke phases. Eleven male swimmers performed a 15 m front crawl at various swimming velocities. The whole-body centre of mass (CoM) was estimated from individual digital human models using inverse kinematics. The horizontal CoM velocity and vertical CoM position from the water surface were calculated for one stroke cycle and divided into five stroke phases: entry, pull, push, release, and recovery. Linear mixed-effects model analysis revealed a positive trend between the mean swimming velocity and the mean vertical CoM position for each stroke phase (p < 0.001 for all phases). The interaction term between stroke phase and swimming velocity was significant (p < 0.001), and the slopes of the propulsive phases (pull and push) were larger than those of the non-propulsive phases (entry, release, and recovery) (p < 0.001). These findings provide practical implications that vertical body position can be evaluated independently of the stroke phase while considering velocity effects, and that focusing on propulsive phases allows easier detection of vertical body position changes.

Implementation of Inverse Kinematics System in Robotic Arm for Glass Pick and Place Operations

Implementation of Inverse Kinematics System in Robotic Arm for Glass Pick and Place Operations

A Review on Inverse Kinematics, Control and Planning for Robotic Manipulators With and Without Obstacles via Deep Neural Networks

Robotic manipulators are highly valuable tools that have become widespread in the industry, as they can achieve great precision and velocity in pick and place as well as processing tasks. However, to unlock their complete potential, some problems such as inverse kinematics (IK) need to be solved: given a Cartesian target, a method is needed to find the right configuration for the robot to reach that point. Another issue that needs to be addressed when dealing with robotic manipulators is the obstacle avoidance problem. Workspaces are usually cluttered and the manipulator should be able to avoid colliding with objects that could damage it, as well as with itself. Two alternatives exist to do this: a controller can be designed that computes the best action for each moment given the manipulator’s state, or a sequence of movements can be planned to be executed by the robot. Classical approaches to all these problems, such as numeric or analytical methods, can produce precise results but take a high computation time and do not always converge. Learning-based methods have gained considerable attention in tackling the IK problem, as well as motion planning and control. These methods can reduce the computational cost and provide results for every situation avoiding singularities. This article presents a literature review of the advances made in the past five years in the use of Deep Neural Networks (DNN) for IK with regard to control and planning with and without obstacles for rigid robotic manipulators. The literature has been organized in several categories depending on the type of DNN used to solve the problem. The main contributions of each reference are reviewed and the best results are presented in summary tables.

Singularity Analysis and Research on the Mechanism Workspace with Three Degrees of Freedom

This article discusses the mechanism of parallel structure, which includes hinged parallelograms. These mechanisms have a certain peculiarity when composing kinematics equations, consisting in the fact that some of the equations have a linear form. This simplifies the system of coupling equations as a whole. By solving direct and inverse kinematics, we will determine the size and shape of the working area. A method was chosen by solving the inverse kinematics to determine the workspace. The size and shape of the working area of the mechanism under consideration with three degrees of freedom are experimentally determined under given initial conditions. The presence of a large working area allows us to recommend this mechanism for use in various branches of robotics, medicine, simulators, etc. The Jacobian matrix of the coupling equations of the mechanism is written out to determine the singularities.

Comparison of Classical, Neural Network and Hybrid Models for Hysteretic Single-tendon Catheter Kinematics.

While robotic control of catheter motion can improve tip positioning accuracy, hysteresis arising from tendon friction and flexural deformation degrades kinematic modeling accuracy. In this paper, we compare the capabilities of three types of models for representing the forward and inverse kinematic maps of a clinical single-tendon cardiac catheter. Classical hysteresis models, neural networks and hybrid combinations of the two are included. Our results show that modeling accuracy is best when models are trained using motions corresponding to the anticipated clinical motions. For sinusoidal motions, recurrent neural network models provide the best performance. For point-to-point motions, however, a simple backlash model can provide comparable performance to a recurrent neural network.

Generalization of inverse kinematics frameworks based on deep learning to new motor tasks and markersets

Generalization of inverse kinematics frameworks based on deep learning to new motor tasks and markersets

The global convergence of some self-scaling conjugate gradient methods for monotone nonlinear equations with application to 3DOF arm robot model.

Conjugate Gradient (CG) methods are widely used for solving large-scale nonlinear systems of equations arising in various real-life applications due to their efficiency in employing vector operations. However, the global convergence analysis of CG methods remains a significant challenge. In response, this study proposes scaled versions of CG parameters based on the renowned Barzilai-Borwein approach for solving convex-constrained monotone nonlinear equations. The proposed algorithms enforce a sufficient descent property independent of the accuracy of the line search procedure and ensure global convergence under appropriate assumptions. Numerical experiments demonstrate the efficiency of the proposed methods in solving large-scale nonlinear systems, including their applicability to accurately solving the inverse kinematic problem of a 3DOF robotic manipulator, where the objective is to minimize the error in achieving a desired trajectory configuration.

Inverse kinematics solution method based on workspace analysis and iterative step coefficient adjustment of general robot

Inverse kinematics solution method based on workspace analysis and iterative step coefficient adjustment of general robot

基于改进RRT算法的葡萄采摘机械臂路径规划研究

The robot's operation in a grape orchard environment is often disrupted by obstacles such as vines and leaves, resulting in low fruit picking efficiency. To achieve stable obstacle avoidance, an improved RRT algorithm based on global adaptive step size and target-biased sampling was developed. First, the kinematic equations of the grape-picking robotic arm were established using the PoE method, and both forward and inverse kinematics calculations were performed to determine the robot's workspace. Then, to address the issues of lack of target orientation and other shortcomings in the traditional RRT algorithm when planning collision-free paths, dynamic updating and global adaptive step size strategies were proposed. Simulation experiments conducted using MATLAB software demonstrated that our improved RRT algorithm, compared to the RRT, RRT_informed, and RRT_star algorithms, offered advantages in terms of lower planning time, fewer sampling points, and shorter path lengths in both 2D and 3D map scenarios. Finally, grape-picking experiments were conducted in both a laboratory setting and a real orchard. The results demonstrated that the average path planning time using the proposed algorithm was shorter compared to baseline algorithms, effectively validating the efficiency and practicality of the algorithm.

Inverse and Forward Kinematics and CAD-Based Simulation of a 5-DOF Delta-Type Parallel Robot with Actuation Redundancy

This article introduces a novel modification of a Delta-type parallel robot. The robot has five degrees of freedom and provides its end-effector with a 3T2R motion pattern (three translational and two rotational degrees of freedom). The fifth degree of freedom (rotation) is kinematically decoupled from the other four motions, and it is controlled by two drives. Thus, the proposed robot has a redundant actuation. In this article, we present an algorithm to solve the inverse kinematics of this robot and apply it to a path modeling example of a spiral-like trajectory. Numerical simulations illustrate the algorithm and show how the actuated coordinates change along the considered trajectory. Forward kinematics follows next, and an approach is introduced to determine all end-effector configurations for the specified displacements in the actuated joints. A numerical example presents four assembly modes of the robot corresponding to four real solutions of the forward kinematic problem. Finally, this article demonstrates a computer-aided design and analysis of the proposed robot: we describe a procedure for analyzing inverse kinematics and calculating actuation torques. This study forms the basis for the future manufacturing and experimental analysis of a robot prototype.

Camera-based control system of a planar cable-driven parallel robot intended for functional rehabilitation

Abstract This paper investigates a closed-loop visual servo control scheme for controlling the position of a fully constrained cable-driven parallel robot (CDPR) designed for functional rehabilitation tasks. The control system incorporates real-time position correction using an Intel RealSense camera. Our CDPR features four cables exiting from pulleys, driven by AC servomotors, to move the moving platform (MP). The focus of this work is the development of a control scheme for a closed-loop visual servoing system utilizing depth/RGB images. The developed algorithm uses this data to determine the actual Cartesian position of the MP, which is then compared to the desired position to calculate the required Cartesian displacement. This displacement is fed into the inverse kinematic model to generate the servomotor commands. Three types of trajectories (circular, square, and triangular) are used to test the controller’s compliance with its position. Compared to the open-loop control of the robot, the new control system increases positional accuracy and effectively handles cable behavior, various perturbations, and modeling errors. The obtained results showed significant improvements in control performance, notably reduced root mean square error and maximal error in terms of position.