Inverse Kinematic Model Research Articles

A practical method for the deformation of long‐stroke hydraulic manipulators in grasping‐handling tasks

AbstractLong‐stroke hydraulic manipulators are utilized in various grasping‐handling tasks, and the flexible deformation of these manipulators is the primary obstacle that affects precise position control of the end‐effectors in Cartesian space. This deformation is manifested in the following three aspects: joint deformation, structural deformation and clearance variation. Due to deformation uncertainty, methods that model the hydraulic manipulator as a combination of flexible multibody systems and hydraulic actuators are unsuitable. In this article, we propose an incremental inverse kinematics model (IIKM) as a new approach to solving the above deformation difficulties. The projection method is used to obtain the inverse kinematic analytical solution of long‐stroke hydraulic manipulators, which is based on the manipulator deformation in the current configuration (current configuration refers to the arrangement of the manipulator links when the manipulator starts to move to the target position). The proposed method avoids complex flexible multibody modeling and parameter identification, allowing long‐stroke hydraulic manipulators to be accurately controlled within a certain neighborhood. An evaluation coefficient is proposed to analyze the calculation accuracy of the IIKM in combination with the success rate obtained from 190 grasping experiments. Through these experiments, we determine the optimal calculation height range of the IIKM in the vertical direction and the optimal calculation position area in the horizontal direction and prove that the IIKM result can guarantee the success of grasping‐handling tasks when the end‐effector is within the optimal calculation height range.

Kinematics modeling of six degrees of freedom humanoid robot arm using improved damped least squares for visual grasping

<span lang="EN-US">The robotic arm has functioned as an arm in the humanoid robot and is generally used to perform grasping tasks. Accordingly, kinematics modeling both forward and inverse kinematics is required to calculate the end-effector position in the cartesian space before performing grasping activities. This research presents the kinematics modeling of six degrees of freedom (6-DOF) robotic arm of the T-FLoW humanoid robot for the grasping mechanism of visual grasping systems on the robot operating system (ROS) platform and CoppeliaSim. Kinematic singularity is a common problem in the inverse kinematics model of robots, but. However, other problems are mechanical limitations and computational time. The work uses the homogeneous transformation matrix (HTM) based on the Euler system of the robot for the forward kinematics and demonstrates the capability of an improved damped least squares (I-DLS) method for the inverse kinematics. The I-DLS method was obtained by improving the original DLS method with the joint limits and clamping techniques. The I-DLS performs better than the original DLS during the experiments yet increases the calculation iteration by 10.95%, with a maximum error position between the end-effector and target positions in path planning of 0.1 cm.</span>

Modeling and control strategy of a haptic interactive robot based on a cable-driven parallel mechanism

Abstract. This study proposes a haptic interactive robot (HIR) configuration and a control strategy based on a cable-driven parallel mechanism. The ball screw drives the cable to improve the motion control accuracy. The robot system control strategy improves the accuracy and stability of haptic interaction. Through configuration optimization design and analysis, eight cables are used to ensure that the robot end effector exerts force and enables motion. Moreover, a forward and inverse kinematics model of the robot is developed. According to the configuration of the HIR, an improved cable tension distribution algorithm can facilely determine the cable tension. Hence, each cable is consistently in a tight state, and the change in tension is not sudden. Drive unit and robot system control strategies are proposed to render the haptic interaction accurate and stable. A simulation experiment of a complex space motion track is implemented through the robot end effector, thus verifying the accuracy of the established forward and inverse kinematics model. The accuracy of the tension distribution algorithm, control strategy, and robot stability are verified through simulation experiments, considering different forces and motion tracks of robot end effectors.

Performance Analysis of Hybrid Kinematic Mechanism for Fusion Reactor Maintenance

The hybrid kinematic mechanism (HKM) as an alternative remote handling subsystem of the Demonstration Fusion Power Plant (DEMO) breeding blanket (BB) is undergoing extensive theoretical analysis and feasibility verification. In this paper, the forward and inverse kinematic models of the HKM are derived by combining the Newtonian iterative method and the analytical method. Cartesian space trajectory planning is designed based on the trajectories of the HKM lifting of inboard and outboard BBs. The continuous smooth inverse kinematic solutions in the HKM joint space are obtained based on the polynomial interpolation method. For the characteristics of the HKM piston thread driving, the end-effector position error caused by the degradation of the spherical joint into a universal joint is analyzed and calculated. During the lifting of the left inboard BB, there is a maximum absolute error ∆P = 3.1 mm, and as the error continues to expand to the bottom of the BB it causes a risk of collision. Combining the overall effects of driving control, rigid–flexible coupling, etc., on position accuracy, an open-loop variable parameter error compensation plan based on the Levenberg–Marquardt (LM) nonlinear damping least-squares algorithm is proposed and validated in this paper. The simulation results show that the maximum absolute error after compensation is less than 1 mm as the mesh density increases, and the absolute position accuracy can be further improved by local mesh encryption. This study verifies the feasibility of the HKM as a BB remote handling subsystem and provides an option for high-precision control of the HKM.

Design and assessment of a micro-nano positioning hexapod platform with flexure hinges for large aperture telescopes.

In order to compensate the optical system bias, which is caused by the change of elevation angle and thermal gradient during the optical alignment of the telescope, a novel high stiffness micro-nano positioning hexapod platform with flexure hinges is proposed in this paper. The novel flexure hinge has a mechanical limit, and its equivalent model is established and analyzed. In addition, in order to speed up the solution process, a novel simplified inverse kinematic model is developed based on the rigid body kinematic theory. Then, an effective rigid-flexible coupling simulation system is built to verify the correctness and applicability of the inverse kinematic model. Finally, a systematic experimental test method and a statistical-based data analysis theory are proposed. The experimental results show that the resolution and repeatability of translation and rotation and lateral stiffness are as follows: 0.3 mm and 0.5arc sec, ± 0.5µm and ±0.5 arc sec, 131.6N⋅µm-1 and 133.0N⋅µm-1. The proposed hexapod platform can be used to correct the optical system bias of large-aperture telescopes.

Analysis of an all-terrain tracked robot with innovative suspension system

Tracked robots are gaining interest in the current context of off-road mobile robotics, especially in soft-terrain applications, because they offer a large contact area with the ground, which provides better traction than wheels or legs. In this paper, a novel passively articulated tracked robot is introduced. On each side, Polibot features a rubber track wrapped around four independently suspended road wheels, an idler wheel, and the sprocket, and it can adapt to many kinds of terrain. An inverse kinematic model for the proposed architecture is also developed to predict the system configuration given the terrain topography. The analytical model is based on constraint equations and a quasi-static force analysis, and it is validated against results obtained from the real prototype, showing good agreement. It can also be used to evaluate the influence of the many suspension design parameters and operating conditions, proving that it can be a useful tool for the proper setup of the robot.

Kernel extreme learning machine‐based general solution to forward kinematics of parallel robots

AbstractThe forward kinematics of parallel robots is a challenging issue due to its highly coupled non‐linear relation among branch chains. This paper presents a novel approach to forward kinematics of parallel robots based on kernel extreme learning machine (KELM). To tackle with the forward kinematics solution of fully parallel robots, the forward kinematics solution of parallel robots is equivalently transformed into a machine learning model first. On this basis, a computational model combining sparrow search algorithm and KELM is then established, which can serve as both regression and classification. Based on SSA‐optimised KELM (SSA‐KELM) established in this study, a binary discriminator for judging the existence of the forward kinematics solution and a multi‐label regression model for predicting the forward kinematics solution are built to obtain the forward kinematics general solution of parallel robots with different structural configurations and parameters. To evaluate the proposed model, a numerical case on this dataset collected by the inverse kinematics model of a typical 6‐DOF parallel robot is conducted, followed by the results manifesting that the binary discriminator with the discriminant accuracy of 88.50% is superior over ELM, KELM, support vector machine and logistic regression. The multi‐label regression model, with the root mean squared error of 0.06 mm for the position and 0.15° for the orientation, outperforms the double‐hidden‐layer back propagation (2‐BP), ELM, KELM and genetic algorithm‐optimised KELM. Furthermore, numerical cases of parallel robots with different structural configurations and parameters are compared with state‐of‐the‐art models. Moreover, these results of numerical simulation and experiment on the host computer demonstrate that the proposed model displays its high precision, high robustness and rapid convergence, which provides a candidate for the forward kinematics of parallel robots.

Inverse Kinematic Modelling of a 3-DOF Robotic Manipulator using Hybrid Deep Learning Models

As the degrees of freedom (DOF) for a manipulator rise, so does the complexity of inverse kinematic modeling. This research provides an inverse kinematic model mapped with the aid of a Multilayer Deep Neural Network (DNN) trained using a unique meta-heuristic approach, namely the Gannet Optimization Algorithm (GOA), to decrease the computational weight and time lag for desired output transformation. The suggested design can automatically pick up on the kinematic characteristics of the manipulator. The sole observational basis for repeated learning is the link between input and output. Using the Robot Operating System (ROS), related simulations on a 3-DOF manipulator are performed. The simulation-generated dataset is split 65:35 for the purpose of training and testing the suggested model. Cost, time for the training data, mean relative error, normal mean square error, and mean absolute error for the test data are the metrics utilized for model validation. The efficacy and superiority of the suggested method are demonstrated by a comparison of the GOA-DNN model with the particle swarm optimization (PSO)-DNN and Grey Wolf Optimization (GWO)-DNN meta-heuristic DNN models.

Hand Gesture Recognition Based on Point Cloud Sequences and Inverse Kinematics Model

Hand Gesture Recognition Based on Point Cloud Sequences and Inverse Kinematics Model

Industrial Cross-Robot Transfer Learning

With increased complexity and individualization of processes and products, the manufacturing industry experiences growing pressure to automate. To meet these constantly growing requirements regarding the flexibility of production and logistic systems, intelligent robotic systems that can adapt dynamically to changing process conditions are essential. Deep reinforcement learning provides a solution and offers a paradigm of learning adaptive control strategies through autonomous agents. However, the corresponding training procedure is based on trial-and-error interactions with an environment and is thus very data inefficient and costly in real-world scenarios. Transfer learning approaches can mitigate these costs and improve learning by leveraging control capabilities acquired in other settings, such as simulations, or from previously learning a different task. In this paper, we investigate the transfer of trajectories between different robot models. We propose a methodology that maps trajectory demonstrations from a source robot into trajectories of a target robot by using forward and inverse kinematics models. We introduce a similarity measure, that one the one hand compares different robot models using key poses along the kinematic chains, and on the other hand captures the efficiency of transitions. This similarity measure is used to select from the mapped trajectories those which are feasible, efficient and retain the general shape of the robotic arm. Finally, we perform behavioral cloning to pre-train a deep reinforcement learning policy for the target domain and demonstrate significant benefits compared to learning the target policy from scratch.

An Inverse Kinematic Model for the 3-PRS Compliant Parallel Mechanism With Forward Causative Force

An Inverse Kinematic Model for the 3-PRS Compliant Parallel Mechanism With Forward Causative Force

Deep Convolutional Generative Adversarial Network for Inverse Kinematics of Self-Assembly Robotic Arm Based on the Depth Sensor

In this study, we propose a new deep convolutional generative adversarial kinematics network (DCGAKN) to establish inverse kinematics of self-assembly robotic arm. We design that the robot system uses a depth sensor detecting an object by you only look once v4 (YOLOv4) algorithm, and then, our proposed DCGAKN is with generator and discriminator of adversarial evolution training inverse kinematics model for controlling self-assembly robotic arm to solve the limited solution space to be more adaptive in the dynamic environment. The following are advancements of our proposed method: 1) generator neural network is trained by few-shot training data to control the self-assembly robotic arm to achieve high-accuracy position; 2) generator is evaluated with discriminator not only depending on training data but also on adaptive evolutionary; 3) the self-assembly robotic arm is like humanoid arm not traditional robotic arm structure and it is easy for self-assembly model to build inverse kinematics without computing inverse kinematics matrix; 4) the object is detected by the depth information based on YOLOv4; and 5) through generator evolutionary, the activity range of robotic arm is not limited with training data range. The proposed DCGAKN is compared with convolutional neural network (CNN) and deep neural network (DNN) that the accuracy rate and distance error achieve 87% and 1.26 cm, respectively. The source code of this work is at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/YiZengHsieh/DCGAKN</uri> .

Unsupervised Learning-based Analytical Inverse Kinematics for High-redundancy Mobile Manipulators

Redundant manipulators are of interest to the robotics community because they can perform additional tasks beyond the primary ones. Coupling them with a mobile platform increases the number of degrees of freedom (DOF) and allows them to perform more complex tasks in a larger workspace. However, the increase in the number of DOFs significantly complicates the establishment of the inverse kinematic model. Indeed, several joint configurations can be associated with a given end-effector (EE) pose. In this paper, we parameterize some joints of the mobile manipulator (MM) to transform it into a non-redundant counterpart by clustering its workspace and configuration space. Thus, conventional methods can be applied to derive the IK of the resulting non-redundant counterpart. A series of simulations validate the proposed approach.

The Caturo: A Submillimeter Diameter Glass Concentric Tube Robot with High Curvature

Concentric tube robots (CTRs) are considered promising in medical applications involving intracorporeal confined spaces, high dexterity, and 3D path following. Most CTRs are currently made of Nitinol due to its superelastic properties and biocompatibility. Herein, glass–polymer composite tubes to ease the shape setting, decrease friction, and benefit linear elasticity are proposed. A new generation of CTR: Caturo, which is made of flexible and miniaturized submillimeter glass–polymer tubes, is presented. Caturo has also an unprecedented radius of curvature that reaches as low as 5 mm. It is fabricated through the thermal treatment of a thin heat‐shrink polymer tube on a glass capillary in a 3D‐printed mold. The fabrication process is quick, versatile, and relatively low cost. Various free space deployment operations are demonstrated, such as 3D helical precurved tube deployment, conical–spiral deployment, and constrained 3D deployment through needle orifices. The forward kinematic model (FKM), inverse kinematic model (IKM), and stability analysis are experimentally validated. Finally, precurved optic fiber deployment with laser emission capability, fluid sample suction, and delivery operation is proposed. There is also a proposed vitreoretinal surgery using a fish‐eye under optical coherence tomography (OCT) visualization.

Trajectory planning of lower limb rehabilitation action for cable-driven rehabilitation robots

In view of the problem that the current research rarely pays attention to the planning of actual rehabilitation actions, a trajectory planning strategy of the lower limb rehabilitation actions for cable-driven rehabilitation robots (CDRRs) is presented in this paper. First, the inverse kinematics model and forward kinematics model of the patient’s lower limb meeting the needs of rehabilitation training were established. Then, a direct solution method based on space geometry was proposed according to the state and constraints of the patient. The mapping from the patient’s lower limb end point trajectory to the patient’s lower leg trajectory was realized by the above two ways. Finally, considering of the movement of the winders, the inverse kinematics model of the reconfigurable CDRR was derived. Through a reasonable cable distribution, patient’s lower leg was regarded as the mobile platform of the CDRR, and the mapping between the ankle joint trajectory of the patient’s lower limb and the cable length change of the CDRR was finally realized. Taking flexion knee joint rehabilitation action as an example, it is shown that this trajectory planning strategy can plan the trajectory of the required rehabilitation action in a three-dimensional space in the simulation and experimental results.

Combined algorithms for analytical inverse kinematics solving and control of the Q-PRR aerial manipulator*

This article presents the design and modeling of a new aerial manipulator system, called Q-PRR, composed of three joints with a fixed base in the center of mass of the multirotor considered as a whole system. This structure has a prismatic joint as a first joint which allows to keep the center of gravity of the Q-PRR as close as possible to the center of gravity of the multirotor. This will also allow to reduce the influence of arm motion on the multirotor roll thus to ensure the stability of the system on trajectory tracking with dynamic changes in the multirotor’s center of gravity. Furthermore, the configuration of the manipulator arm for the desired position of the end-effector given by the inverse kinematics model is kept without any change in the position and attitude of the multirotor. This article develops both forward and inverse kinematics models for a nonlinear underactuated system using the Denavit–Hartenberg notation. When a new algorithm is presented for the inverse kinematics based on Levenberg–Marquardt algorithm. Then, the dynamic model in the joint spaces is developed with the Lagrangian formalism. The Q-PRR is controlled using a model-free control with a comparison of two states, a free fly and disturbance forces applied to the whole system with manipulator arm movement.

A novel visual servo method for space manipulators by planning jerk

A novel trajectory planning method for space manipulators is proposed in this article, which can generate trajectory in Cartesian space with continuous joint jerk. The key idea is that, given the desired position for an individual joint, the corresponding joint trajectory is generated in a way like a controller. The generated jerk acts as the controller’s output driving an ideal third-order system to arrive at the desired position, with no need for discrete points in advance. In real applications, the visual servo task is accomplished hierarchically. Since the desired pose in Cartesian space measured by cameras concerns multi degrees of freedom (DOF), desired positions for individual joints are obtained by inverse-kinematics model. Then, joint trajectories are generated as above. To improve the trajectory’s smoothness, a bridging matrix is implemented to ensure that the desired pose varies continuously. Simulation and experimental results show that the proposed method is effective to track targets with different kinds of motion, i.e. can track the input-bounded signal asymptotically.

One key issue in inverse kinematic modeling of lower mobility serial mechanisms

• The D-H parameters method for establishing pose coupling equations of serial mechanisms is proposed. • The terminal pose coupling equations of two serial mechanisms are established in closed form. • The feasible positions and orientations of the end-effector for serial mechanisms are determined. The inverse kinematics of lower mobility serial mechanisms is an important problem, but it has not been well solved actually. The feasible six dimension pose of the end-effector can not be reasonably given, which leads to the defect of the inverse solution model of this kind of mechanisms. In this paper, this problem is solved based on establishing the pose coupling equations using D -H(Denavit–Hartenberg) parameters method. The complete inverse kinematics of two 5-DOF(Degree of freedom) serial mechanisms considering pose coupling equations are established to illustrate the improved inverse kinematic modeling process. The results show that the serial mechanisms always have highly coupled nonlinear pose coupling equations which can be effectively used in determining the feasible pose of the end-effector. The proposed inverse kinematics producer is applicable for other serial mechanisms.

A bioinspired fishbone continuum robot with rigid-flexible-soft coupling structure

Rigid-flexible-soft coupled robots are an important development direction of robotics, which face many theoretical and technical challenges in their design, manufacture, and modeling. Inspired by fishbones, we propose a novel cable-driven single-backbone continuum robot which has a compact structure, is lightweight, and has high dexterity. In contrast to the existing single-backbone continuum robots, the middle backbone of the continuum robot is serially formed by multiple cross-arranged bioinspired fishbone units. The proposed bioinspired fishbone unit, having good one-dimensional bending properties, is a special rigid-flexible-soft structure mainly made by multi-material 3D printing technology. The unique design and manufacture of the middle backbone provide the continuum robot with excellent constant curvature characteristics and reduce the coupling between different motion dimensions, laying a foundation for the continuum robot to have a more accurate theoretical model as well as regular and controllable deformation. Moreover, we build the forward and inverse kinematics model based on the geometric analysis method, and analyze its workspace. Further, the comparison between the experimental and theoretical results shows that the prediction errors of the kinematics model are within the desired 0.5 mm. Also, we establish the relation between the cable driving force of the bioinspired fishbone unit and its bending angle, which can provide guidance for the optimization of the continuum robot in the future. The application demos prove that the continuum robot has good dexterity and compliance, and can perform tasks such as obstacle crossing locomotion and narrow space transportation. This work provides new ideas for the bioinspired design and high-precision modeling of continuum robots.

Sim2real kinematics modeling of industrial robots based on FPGA-acceleration

It is challenging to apply robotic inverse kinematics directly in simulation to real robot motion due to the multi-configuration problem, robot end deviation, and delay. To this end, this paper proposes a industrial robot modeling method for “Simulation to Real” to obtain the real joint angle vector given real end-effector target pose . In the first stage, the joint angle in simulation corresponding to the real end-effector pose, is calculated by improved Newton–Raphson method. In the second stage, a data-driven ResNet model, which is trained on enough simulated joint angles and end-effector pose obtained in the first stage, is used to acquire the end-effector pose in simulation. The joint angles in real environment can be obtained by the simulated end-effector pose in execution stage. The experiment takes a six-axis industrial robot as a case study. The results verify the feasibility and real-time performance of the inverse kinematics method in robotic motion from simulation to real environment. • A sim2real modeling method for the kinematics of industrial robots is proposed. • An FPGA-accelerated inverse kinematics model based on iteration is built. • An FPGA-accelerated forward kinematics model in simulation based on ResNet is built.