Inverse Kinematic Analysis Research Articles

Research on Pose Error Modeling and Compensation of Posture Adjustment Mechanism Based on WOA-RBF Neural Network

This paper is aimed to address the issue of decreased accuracy in the ship block docking caused by the structural errors of posture adjustment mechanism. First, inverse kinematic analysis is performed to investigate the sources of static errors in the mechanism. Subsequently, based on the closed-loop vector method, a pose error model for the moving platform is established, which includes eight categories of error terms. The impact of various structural errors on the pose accuracy of the moving platform is then compared and analyzed under both single-limb and multi-limb configurations. Therefore, a compensation method based on the whale optimization algorithm optimized radial basis function neural network is proposed. By transforming pose errors into actuator length errors, it establishes a predictive model between the theoretical pose of the dynamic platform and actuator length errors. After optimizing the network parameters, it yields the actuator length compensation to correct the actual pose of the dynamic platform. Simulation and experimental results validate the effectiveness of this method in enhancing the motion accuracy of the parallel mechanism. The mean pose accuracy of the moving platform is improved by 85.07%, demonstrating a significant compensation effect.

Development of 3-USR Type Spatial 6-DOF Parallel Mechanism with Large Workspace—Proposal of 3-USR Mechanism and Design of Spherical 5-Link Mechanism to Replace Active Universal Pairs (U)—

In this paper, we present a 3-USR-type 6-degree-of-freedom (6-DOF) parallel mechanism characterized by an expansive workspace. This mechanism consists of three coupling chains, each comprising a universal pair (U), a spherical pair (S), and a rotational pair (R), in sequence from the stationary base. To achieve a large workspace, the three stationary universal pairs are designated as active pairs, with their rotational axes intersecting at a single point, thereby creating a hemispherical workspace akin to that of a serial mechanism. To implement the active universal pair within a parallel mechanism, we replaced the spherical 5-link mechanism, which originally featured two stationary active rotational pairs, with stationary active universal pairs, and conducted an inverse kinematics analysis. Subsequently, we assessed the expansive workspace of the spherical 5-link mechanism, a key factor influencing the working area of the 3-USR-type 6-DOF parallel mechanism, by examining its motion transmissibility. Based on this evaluation, we designed two compact mechanisms that provide a large workspace and allow for the configuration of all three rotational axes to intersect at a single point. We experimentally investigated the relationship between motion transmissibility and rigidity for these two types of spherical 5-link mechanisms, demonstrating that motion transmissibility can serve as an indicator of rigidity. Finally, a prototype of the 3-USR mechanism was fabricated.

Design and kinematics of a novel rigid-flexible coupling hybrid robot for aeroengine blades in situ repair

PurposeThis paper aims to present the mechanical design and kinematics of a novel rigid-flexible coupling hybrid robot to develop a promising aeroengine blades in situ repair technology.Design/methodology/approachAccording to requirements analysis, a novel rigid-flexible coupling hybrid robot is proposed by combining a three degrees of freedom (DOF) parallel mechanism with a flexible continuum section. Then the kinematics models of both parallel mechanism and flexible continuum section are derived respectively. Finally, based on equivalent joint method, a two-step numerical iterative inverse kinematics algorithm is proposed for the whole robot: (1) the flexible continuum section is equivalently transformed to a 2-DOF spherical joint, thus the approximate analytical inverse kinematic solution can be obtained; (2) the accurate solution is derived by an iterative derivation of both parallel mechanism and flexible continuum section.FindingsTo verify structure scheme and the proposed kinematics modeling method, numerical simulations and prototype experiments are implemented. The results show that the proposed kinematics algorithm has sufficient accuracy and computational efficiency in the whole available workspace, that is end-effector position error and orientation error are less than 0.2 mm and 0.01° respectively, and computation time is less than 0.22s.Originality/valueA novel rigid-flexible coupling hybrid robot for aeroengine blades in situ repair is designed. A two-step numerical iterative inverse kinematics algorithm is proposed for this unique hybrid robots, which has good accuracy and computational efficiency.

Personalizing the shoulder rhythm in a computational upper body model improves kinematic tracking in high range-of-motion arm movements

Musculoskeletal models of the shoulder are needed to understand the mechanics of overhead motions. Existing models implementing the shoulder rhythm are generic and might not accurately represent an individual’s scapular kinematics. We introduce a method to personalize the shoulder rhythm of a computational model of the upper body that defines the orientations of the clavicle and scapula based on glenohumeral joint angles. During five static calibration poses, we palpate and measure the orientation of the scapula. We explore the importance of representing shoulder elevation by introducing clavicle elevation as a degree of freedom that is independent of the glenohumeral angles. For ten subjects, we record the five calibration poses, ten additional static poses, and dynamic arm raises covering the participants’ full range of motion in each body plane using optical motion capture. We examine the data using a dynamically-constrained inverse kinematics analysis. Shoulder rhythm personalization, independent clavicle elevation, and both in combination reduce the average upper body marker tracking error compared to the generic model in the static poses (26 mm to 17–20 mm) and in the dynamic trials (22 mm to 14–17 mm). Only personalization reduces the average scapula marker error (51 mm to 36–38 mm) and scapula axis-angle error (15° to 10°) compared with the palpated ground truth measurements in the static poses, and in the dynamic trials at instances that best match the static poses (53 mm to 37–40 mm, 15° to 9°). Our results show that personalizing upper body models improves kinematic tracking. We provide our experimental data, model, and methods to allow researchers to reproduce and build upon our results.

Development of a Six-Degree-of-Freedom Deep-Sea Water-Hydraulic Manipulator

With the advancement of deep-sea exploration, the demand for underwater manipulators capable of long-duration heavy-duty operations has intensified. Water-hydraulic systems exhibit less viscosity variation with increasing depth than oil-based systems, offering better adaptability to deep-sea conditions. Using seawater as the driving medium inherently eliminates issues such as oil contamination by water, frequent maintenance limiting underwater operation time, and environmental pollution caused by oil leaks. This paper introduces a deep-sea manipulator directly driven by seawater from the deep-sea environment. To address the challenges of weak lubrication and high corrosion associated with water hydraulics, a reciprocating plunger seal was adopted, and a water-hydraulic actuator was developed. The installation positions of actuator hinges and maximum output force requirements were optimized using particle swarm optimization (PSO), effectively reducing the manipulator’s self-weight. Through kinematic and inverse kinematic analyses and joint performance tests, a six-degree-of-freedom water-hydraulic manipulator was designed with a maximum reach of 2.5 m, a lifting capacity of 5000 N, and end-effector positioning accuracy within 18 mm.

Kinematics analysis and simulation of bionic five-finger manipulator

Abstract To address the complexity of traditional manipulator structures, a bionic five-finger manipulator has been designed and developed. After introducing the manipulator’s structure and functionality, the kinematic model was established using the D-H parameter method, followed by the analysis of forward and inverse kinematics. The fingertip pose was derived using the homogeneous transformation formula, and the accuracy of the theoretical analysis was validated by comparing the mathematical and MATLAB simulation results. The Monte Carlo method was employed to determine the accessible motion space of the mechanical fingertip. Subsequently, trajectory planning for the manipulator was performed based on known initial and end finger angles. A comparison with ADAMS simulation results demonstrated the rationality of the mechanical finger’s movement space, stable speed, and compliance with actual grasping requirements. This study lays a theoretical foundation for the subsequent parameter optimization of the manipulator.

Modeling and Simulation of a 2SPU-RU Parallel Mechanism for a Prosthetic Ankle with Three Degrees of Freedom

To assist an individual with an amputation in regaining daily quality of life, a 2SPU-RU type parallel mechanism was developed based on ankle biomechanics. The inverse kinematic analysis of this mechanism was performed using the vector method. Subsequently, the Jacobian matrices were analyzed. The dynamic model of the mechanism was then created based on the principle of virtual work, and its theoretical solution was compared with numerical results obtained in a simulation environment. Additionally, the validity of the dynamic model and the inverse kinematics was verified by comparing theoretical and simulation results for the movements of plantarflexion–dorsiflexion, eversion–inversion, and abduction–adduction during the gait cycle.

Trajectory Analysis of 6-DOF Industrial Robot Manipulators by Using Artificial Neural Networks.

Robot manipulators are robotic systems that are frequently used in automation systems and able to provide increased speed, precision, and efficiency in the industrial applications. Due to their nonlinear and complex nature, it is crucial to optimize the robot manipulator systems in terms of trajectory control. In this study, positioning analyses based on artificial neural networks (ANNs) were performed for robot manipulator systems used in the textile industry, and the optimal ANN model for the high-accuracy positioning was improved. The inverse kinematic analyses of a 6-degree-of-freedom (DOF) industrial denim robot manipulator were carried out via four different learning algorithms, delta-bar-delta (DBD), online back propagation (OBP), quick back propagation (QBP), and random back propagation (RBP), for the proposed neural network predictor. From the results obtained, it was observed that the QBP-based 3-10-6 type ANN structure produced the optimal results in terms of estimation and modeling of trajectory control. In addition, the 3-5-6 type ANN structure was also improved, and its root mean square error (RMSE) and statistical R2 performances were compared with that of the 3-10-6 ANN structure. Consequently, it can be concluded that the proposed neural predictors can successfully be employed in real-time industrial applications for robot manipulator trajectory analysis.

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.

Visual Servo Algorithm Of Robot Arm For Pick And Place Application

The robot arm is a device consisting of a moving chain of links connected by joints. Electrical motors are frequently used to move each robot arm joint. An end-effector that can move freely in space is usually attached to one end of the robot platform, which is fixed. Robot arms can do repetitive operations at rates and precision far exceeding human operators. Nowadays, robot arm systems are widely used worldwide to increase the quality and efficiency of the manufacturing process in the industry. Typical applications of the robot arm system are assembly, painting, welding, pick and place operation, and others. Besides, many industries employ robot arms for various jobs such as selecting and putting, painting, and material handling. However, one of the most challenging issues in completing these jobs is determining the target location of the robot arm's end-effector. There are two different methods for analyzing the robot arm's movement: forward and inverse kinematic analysis. Based on the visual servo algorithm, this study uses inverse kinematics to execute the pick and place operation. First, an object recognition algorithm is implemented to identify the object to be grasped. Then, an algorithm to avoid any obstacles is done. The study's findings show that good system performance has been obtained in all three algorithms: first, object recognition algorithm, second, obstacle avoidance algorithm, and lastly, visual servobased pick and place operation. Thus, it can be concluded that the robot arm's visual servo algorithm is suitable for pick-and-place applications.

Inverse kinematics analysis of a wrist rehabilitation robot using artificial neural network and adaptive Neuro-Fuzzy inference system

Inverse kinematics analysis of a wrist rehabilitation robot using artificial neural network and adaptive Neuro-Fuzzy inference system

Navigating Arm Robot Motion with Vision via Image Processing

The integration of vision-based technology in daily life has been perceived in recent years. The wide range of robot implementations, including agriculture, necessitates the installation of the eye to fully function and substitute the human eye. Camera and image processing work hand in hand in realizing the idea of automatic labor in daily life. This paper presents an arm robot navigated by image processing inputs from objects' coordinate positions. The robot is assigned to pick and place red tomatoes within different lighting and positions. The robot can sort the red tomatoes and ignore the green tomatoes. Inverse kinematics analysis is included to show the effectiveness of the proposed method. This robot is ideal for agricultural settings to sort the harvested fruit.

Structure design and coordinated motion analysis of bionic crocodile robot

Crocodiles, one of the oldest and most resilient species on Earth, have demonstrated remarkable locomotor abilities both on land and in water, evolving over millennia to adapt to diverse environments. In this study, we draw inspiration from crocodiles and design a highly biomimetic crocodile robot equipped with multiple degrees of freedom and articulated trunk joints. This design is based on comprehensive analysis of the structural and motion characteristics of real crocodiles. The bionic crocodile robot has a problem of limb-torso incoordination during movement. To solve this problem, we used the D-H method for both forward and inverse kinematics analysis of the robot’s legs and spine. Through a series of simulation experiments, we investigated the robot’s motion stability, fault tolerance, and adaptability to environments in two motor patterns: with and without spine and tail movements. The experimental results show that the bionic crocodile robot exhibits superior motion performance when the spine and tail cooperate with the extremities. This study not only demonstrates the potential of biomimicry in robotics but also underscores the significance of understanding how nature’s designs can inform and enhance technological innovations.

Kinematics Analysis and Trajectory Planning of 6-DOF Hydraulic Robotic Arm in Driving Side Pile

Given the difficulty in manually adjusting the position and posture of the pile body during the pile driving process, the improved Denavit-Hartenberg (D-H) parameter method is used to establish the kinematics equation of the mechanical arm, based on the motion characteristics of each mechanism of the mechanical arm of the pile driver, and forward and inverse kinematics analysis is carried out to solve the equation. The mechanical arm of the pile driver is modeled and simulated using the Robotics Toolbox of MATLAB to verify the proposed kinematics model of the mechanical arm of the pile driver. The Monte Carlo method is used to investigate the working space of the mechanical arm of the pile driver, revealing that the arm can extend from the nearest point by 900 mm to the furthest extension of 1800 mm. The actuator’s lowest point allows for a descent of 1000 mm and an ascent of up to 1500 mm. A novel multi-strategy grey wolf optimizer (GWO) algorithm is proposed for robotic arm three-dimensional (3D) path planning, successfully outperforming the basic GWO, ant colony algorithm (ACA), genetic algorithm (GA), and artificial fish swarm algorithm (AFSA) in simulation experiments. Comparative results show that the proposed algorithm efficiently searches for optimal paths, avoiding obstacles with shorter lengths. In robotic arm simulations, the multi-strategy GWO reduces path length by 16.575% and running time by 9.452% compared to the basic GWO algorithm.

Implementation of singularity-free inverse kinematics for humanoid robotic arm using Bayesian optimized deep neural network

This study presents the efficacy of deep learning techniques in controlling the arm of a humanoid robot without resorting to inverse kinematic analysis. Emphasizing real-time applicability, a straightforward deep neural network (DNN) structure is developed and optimized using hyperparameter Bayesian optimization. The Keras Bayesian Optimization Tuner with Gaussian process fine-tunes the DNN architecture. Utilizing a simulation environment and generating around 10 billion datasets, the optimized DNN is evaluated on both simulated and real robots. Impressively, the trained DNN exhibits a notable ability to predict robot control within a 25 ms timeframe, achieving a total Mean Absolute Error (MAE) of 0.02 for the xyz-axes. These results underscore the potential of deep learning-based approaches in humanoid robot arm control, eliminating the need for inverse kinematic equations and providing valuable insights for future robotic system development.

RRR Robotik Kol için Çok Katmanlı Algılayıcı ve Ters Kinematik Karşılaştırması

In this study, the position control simulation of a 3 Degree of Freedom (3DOF) robot arm was compared with machine learning and inverse kinematic analysis separately. The considered robot arm is designed in RRR pattern. In the inverse kinematic analysis of the robot arm, the geometric approach and the analytical approach are used together. Multi-Layer Perceptron (MLP) was used as a machine learning method. Some of the coordinate data that the robot arm can reach in the working space are selected and the MLP model is trained with these data. When training was done with MLP machine learning method, the correlation coefficient (R2) was obtained as 1. Coordinates of 3 different geometric models (helix, star and daisy) that can be included in the working space are used as test data of the MLP model. These tests are simulated in 3D in MATLAB environment. The simulation results were compared with the inverse kinematics analysis data. As a result, Mean Relative Error (MRE) values for helix, star and daisy shapes were calculated as 0.0007, 0.0033 and 0.0011, respectively, in the tests performed. Mean Squared Error (MSE) values were obtained as 0.0034, 0.0065 and 0.0040, respectively. This confirms that the proposed MLP model can operate this system at the desired stability.

Research on real-time fire detection and locating for automotive firefighting robot in factories based on Convolutional Neural Network

Automotive fire robots that are used in factories can carry out diverse operations in regard to patrolling, fire detection, and programmed fire rescue. An accurate detection of fire sources in factories is significantly crucial for unmanned firefighting robot in terms of building a reliable sensor system. An approach proposed by this paper to recognize the color and dynamic shape of varying flames based on HSV color algorithms and Convolutional Neural Network. As a comparison to traditional RGB image processing, this approach is more efficient in isolating colors in environment and more adaptive to a fire site that includes multiple noise factors. The research in this paper uses image processing algorithms that is trained by CNN to detect flames in simulated factory environments, followed by a HSV color locating algorithm to compute the coordinates of target fire to perform inverse kinematic analysis on an unmanned firefighting robot.

An Origami Continuum Manipulator with Modularized Design and Hybrid Actuation: Accurate Kinematic Modeling and Experiments

Herein, this study contributes significantly to the advancement of continuum manipulators in two main aspects. First, a modularization concept and a hybrid actuation scheme to create a novel origami continuum manipulator with exceptional deformability are introduced. Second, an accurate model and framework for the forward and inverse kinematic analysis of origami manipulators are proposed. Specifically, each origami manipulator module can achieve axial extension and bending deformation by coordinated actuation of shape memory alloy (SMA) and pneumatic muscles, and the manipulator's end is equipped with a deformable gripper based on waterbomb origami and actuated by SMA. Through careful consideration of the self‐weight and torque balance, an accurate kinematic model based on the Denavit–Hartenberg method is established, which enables one to effectively predict the reachable extreme positions and spatial poses of the manipulator and solve the inverse kinematics using a genetic algorithm. Comprehensive experiments are conducted to validate the design's rationality and model's accuracy . In these tests, the rich spatial configurations are not only demonstrated that can be achieved by integrating hybrid actuators with origami modules but also the accuracy and reliability of the kinematic model are confirmed, opening up possibilities for the advancement and application of origami‐inspired robotics in various fields.

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

Analytical Inverse Kinematics for a Prismatic-Revolute Hybrid Joints Radiography Robot Mounted on the Ambulance

Analytical Inverse Kinematics for a Prismatic-Revolute Hybrid Joints Radiography Robot Mounted on the Ambulance