A human system learning model for solving the inverse kinematics problem by direct inverse modeling

A modular neural network architecture for inverse kinematics model learning

Transdermal drug delivery systems are a key technology to administer drugs with a high first-pass effect in a non-invasive and controlled way. Physics-based modeling and simulation are on their way to become a cornerstone in the engineering of these healthcare devices since it provides a unique complementarity to experimental data and additional insights. Simulations enable to virtually probe the drug transport inside the skin at each point in time and space. However, the tedious experimental or numerical determination of material properties currently forms a bottleneck in the modeling workflow. We show that multiparameter inverse modeling to determine the drug diffusion and partition coefficients is a fast and reliable alternative. We demonstrate this strategy for transdermal delivery of fentanyl. We found that inverse modeling reduced the normalized root mean square deviation of the measured drug uptake flux from 26 to 9%, when compared to the experimental measurement of all skin properties. We found that this improved agreement with experiments was only possible if the diffusion in the reservoir holding the drug was smaller than the experimentally measured diffusion coefficients suggested. For indirect inverse modeling, which systematically explores the entire parametric space, 30,000 simulations were required. By relying on direct inverse modeling, we reduced the number of simulations to be performed to only 300, so a factor 100 difference. The modeling approach’s added value is that it can be calibrated once in-silico for all model parameters simultaneously by solely relying on a single measurement of the drug uptake flux evolution over time. We showed that this calibrated model could accurately be used to simulate transdermal patches with other drug doses. We showed that inverse modeling is a fast way to build up an accurate mechanistic model for drug delivery. This strategy opens the door to clinically ready therapy that is tailored to patients.

Computing joint commands in real time by solving an inverse kinematic (IK) problem is an important manipulation task that is commonly encountered in agricultural operations such as fruit harvesting, disease detection and leaf sampling. To date, different numerical and analytical approaches have been proposed, and many of them are able to take constraints into account. In this study, a set of analytical algorithms is investigated to quickly solve the constrained five-degree-of-freedom inverse kinematic problem. The constraints due to actuator limitations and mechanical design of the mechanism are utilised in generating the solution sets for joint variables. Also a suboptimal solution for the inverse kinematic problem within this set is derived. The proposed methods are compared with a typical numerical solution based on non-linear constrained optimisation. It is shown that the proposed analytical and suboptimal semi-analytical solutions can be found in a much quicker fashion as compared to the non-linear constrained optimisation approach. The algorithms are validated by the handling mechanism in a ground robot platform specifically designed to operate in strawberry orchards.

In a controller design process, it is interesting to find the inverse model, in which the desired input signal of the system is determined by using a desired output of the system. In many cases, the inverse model problem is very difficult, and sometimes impossible to determine and implement. There are some methods to make an inverse dynamic model, such as computed torque control, which had been studied in the previous Chapters. The capability of the NNs to learn the inverse model of the plant has been investigated for many years; the NN can be used to learn an approximate inverse system. In this approach, the desired output of the dynamic system should be known, and the NN can be trained by the desired output of the dynamic system to obtain the inverse dynamic model. In early studies of adaptive learning control using an NN model, Barto et al. [1], Jordan [2], Miller et al. [3] and Psaltis et al. [4] addressed the problem of how to obtain the error signal for training the NN controller. Generally, the cost function, consisting of the squared norm of the output reference errors, can not correctly train the NN controller as an inverse dynamic model. Therefore, Jordan [2] proposed a forward-inverse-modeling, and Albus [5], Atkeson and Reinkensmeyer [6], Psaltis et al. [4], and Kuperstein and Rubinstein [7] used a direct-inverse-modeling to obtain the command-error for forming the inverse dynamic model as a feedforward controller. Moreover, Watanabe et al. [8] proposed a linear NN using feedforward NNs with two layers and a linear unit function in the output-layer. Kawato [9] proposed a learning method to obtain a feedforward controller, which uses the output of a feedback controller as the error for training an NN model. The conventional feedback controller is configured in parallel to the NN controller, in the same manner as reported by Gomi and Kawato [10], and Miyamoto et al. [11], but with a different SF and learning algorithm. In another work, Wada and Kawato [12] used a feedforward NN as an inverse dynamic model.

To enhance the performance for high-speed tracking control of a piezoelectric tube actuator (PTA), an improved direct inverse rate-dependent PI model is obtained in this paper, and then adopted to design a practical feedforward controller achieving high-speed tracking control for a high-frequency trajectory with strong robustness. Specifically, based on the Prandtl-Ishlinskii (PI) model, an improved direct rate-dependent inverse model is set up for the PTA, with a polynomial function module introduced to eliminate the influence caused by the structure nonlinearity. Then, considering the features of the PTA structure, some preprocessing procedure is proposed to handle the collected experimental data, with the results utilized to identify all the unknown parameters over a wide bandwidth. Based on the obtained model, a practical feedforward controller, which presents the advantages of high-speed response, simple structure and convenient implementation, is then designed to enable the PTA to track high-frequency trajectories with satisfactory precision. Some experimental results are provided, which clearly demonstrate the high precision of the constructed/identified model, and the satisfactory performance of the proposed practical feedforward tracking control law.

The inverse kinematics (IK) problem is a fundamental problem in robotic manipulation. Traditional, Jacobian-based solutions to this problem are known to scale poorly with the number of degrees of freedom (DOF) in the manipulator, necessitating novel IK solutions for high-DOF manipulators. Metaheuristic optimization algorithms such as Particle Swarm Optimization (PSO) are a promising alternative approach to traditional IK techniques due to their strong performance on difficult and high-DOF problems in many diverse domains. Previous applications of PSO to the IK problem have focused on specific classes (e.g., planar) or models of manipulators or specific IK subproblems (e.g., position only IK). Furthermore, the experimental validation of these techniques has considered only manipulators with seven or fewer degrees of freedom and taken place almost exclusively in simulation. In this paper, we (1) generalize previous work to derive a fitness function that can be minimized to solve the full position and orientation IK problem for any serial manipulator while respecting joint limits and avoiding self-collisions, (2) present the first statistical analysis of PSO as a high-DOF IK solver on simulated manipulators with up to 180 DOF using this fitness function, and (3) present an important validation of PSO-based IK using this fitness function on real-world hardware on a difficult precision manipulation task.

ANFIS-Inverse-Controlled PUMA 560 Workspace Robot with Spherical Wrist

PurposeThis paper aims to develop a new real‐time effective method for solving the inverse kinematics (IK) problem, especially for those manipulators with high‐dimensional nonlinear kinematic equations.Design/methodology/approachThe paper transforms the IKs problem into a minimization problem. Then, a novel meta‐heuristic algorithm, called the electromagnetism‐like method (EM), is used to solve this equivalent problem. Moreover, in order to further improve the computational efficiency and accuracy of EM, a hybrid method which combines EM with the Davidon‐Fletcher‐Powell (DFP) method is proposed.FindingsThe results showed that EM is a powerful yet easy algorithm for solving the IKs problem of robot manipulators. Its complexity is independent on the characteristics of the kinematic equations involving dimensionality and the degree of nonlinearity. Moreover, EM can be used as an accompanying algorithm for DFP method to get better precision at a lower iteration number.Originality/valueThe method developed in this paper is a generalized approach that is efficient enough to obtain IK solutions independent of robot geometry and the number of degrees of freedom.

This paper presents a comparative study of analytical and deep learning (DL) approaches for solving the inverse kinematics (IK) problem in high-degree-of-freedom (DOF) robotic manipulators, focusing on the Lunar Exploration Rover System (LERS). The IK problem, central to robotic motion control, is traditionally solved using analytical methods, but recent advances in deep learning provide new opportunities to enhance precision and efficiency. The study evaluates the performance of DL Neural Networks, in addressing the complexities of high-DOF manipulators. A detailed comparison is conducted between traditional geometric solutions and DL-based models, with an emphasis on robustness and computational efficiency under noisy conditions. The results demonstrate the potential of DL methods to outperform traditional techniques in high-DOF environments, paving the way for future advancements in autonomous robotic systems. In addition to the IK study, the paper discusses the design and integration of the LERS robotic system, which plays a critical role in advancing autonomous lunar exploration under the ARTEMIS program. As part of this international collaboration involving NASA, the Canadian Space Agency (CSA), the European Space Agency (ESA), and the Japan Aerospace Exploration Agency (JAXA), LERS is tasked with supporting both crewed and unmanned missions on the Moon. LERS is designed to perform precise robotic manipulation tasks such as deploying infrastructure, gathering scientific data, and managing lunar resources, all of which are vital for future missions to Mars. The implementation of advanced IK solutions is key to enabling the precise control of LERS’s robotic arms, allowing for the successful execution of complex tasks such as assembling habitats, handling materials, and conducting scientific analyses on the lunar surface. This work highlights the importance of IK in ensuring that robotic systems like LERS can operate with the precision needed for the next generation of lunar missions.

This paper addresses the imitation procedure of the human arms by a NAO humanoid robot. The exact position of the user arms are obtained based on the position of him/her shoulders, elbows and wrists, which are obtained by using the Microsoft Kinect as a 3-dimensional optical motion capture sensor. Communication between the kinect and computer server is done by using Robot Operating System (ROS) framework. Due to the differences between human and humanoid robot dimensions, captured motion should be mapped on the robot's dimensions. Mapped data is used for kinematic operation. In this paper, the inverse kinematic problem is solved by proposing an approach based on a novel simplified geometric method. In this approach, the distal form of the Denavit-Hartenberg convention of both arms is studied and a simple and fast geometric inverse kinematic solution is proposed. Finally, the proposed method is verified experimentally in Webots simulator and on a real NAO-H25 humanoid robot. Experiments are resulting in a coherent imitation between human arm and NAO with respect to time response.

To simplify the solving process of the inverse kinematics problem of a bionic hand, a method of solving the inverse kinematics problem based on an artificial fish swarm algorithm was proposed in this paper. First, the kinematic equations of the bionic hand were established using the Denavit-Hartenberg (D-H) parameters. By selecting the appropriate decision variables and objective functions, an optimization model was established for solving the inverse kinematics problem of the bionic hand. Then, the artificial fish swarm algorithm was used to search the solution space to find the inverse solution of the position of the bionic hand. The results show that the artificial fish swarm algorithm has good convergence performance in solving the kinematic problem of the bionic hand. Besides, the accuracy of the artificial fish swarm algorithm can be improved by adjusting parameters such as the initial number of fish swarms, visual field, crowding degree, and step size.

Reliability of joint kinematic calculations based on direct kinematic and inverse kinematic models in obese children

This paper considers development of positioning systems for manipulator links to solve the forward kinematics problem (FKP) and inverse kinematics problem (IKP). Here we study a robotic manipulator with four degrees of freedom. It should be noted, that one of the relevant research problems of modern modular robotic devices consists in the lack of the universal algorithms, that would ensure kinematics problem recalculations in the cases of reconfigurations of the whole system. Challenges, the researchers are facing with when solving this problem, have to do with geometrical and non-linear equations (trigonometric equations), finding of inverse matrix of the Denavit—Hartenberg presentation, as well with other problems, such as multiple solutions when using the analytical approach. Common mathematical solutions of the inverse kinematics problem, such as geometric, iterative and algebraic ones, may not always lead to physically appropriate solutions. It’s also noteworthy, that, trying to introduce physical solutions for the manipulator, we need to take into account, that the number of calculation formulas increases, what, in turn, causes further computing power consumption increase. If the manipulator acquires additional degrees of freedom, analytical modeling becomes virtually impossible. One of relevant inverse kinematics solution methods consists in implementation of neural networks to that end. To solve this problem various sources were analyzed, considering alternative ways of target point discovery. Considering the analyzed papers, we propose to use a perceptron. Before training the network, we compose an algorithm, calculating the Denavit—Hartman presentation matrix and check for correctness of target point reach by the terminal manipulator link. We did calculations for a thousand positions of manipulator and object in the environment, fed to the neural network. When solving FKP we obtain object coordinates as network output, whereas in the case of IKP — manipulator link angles. We present kinematic scheme testing results, as well a control scheme for a manipulator with four degrees of freedom.

In this study a five Degrees of Freedom (DOF) serial robot manipulator was designed and implemented. The inverse kinematics problem, which has not exact analytical solution (only one inverse kinematic solution for a predefined end effector position in three dimensional space), of the robot mechanism was solved by using the combination of the analytical method and a simple search method. In order to use the proposed method in real-time applications, the method is designed so that it can be used to solve the inverse kinematics problem for the next point of the end-effector while the mechanism is working. Moreover, so that to control the implemented mechanism, a user interface program was written by using Visual Basic programming language. Finally, the proposed inverse kinematic solution method was tested on two different trajectories, an arc shaped trajectory that composed of 58 points and a linear trajectory divided into 29 points. The obtained results revealed that the proposed method can be used successfully in solving the inverse kinematic problem of the designed mechanism.

The Inverse Kinematics (IK) problem is concerned with finding robot control parameters to bring the robot into a desired position under the kinematics and joint limit constraints. We present a globally optimal solution to the IK problem for a general serial 7DOF manipulator with revolute joints and a polynomial objective function. We show that the kinematic constraints due to rotations can be all generated by the second-degree polynomials. This is an important result since it significantly simplifies the further step where we find the optimal solution by Lasserre relaxations of nonconvex polynomial systems. We demonstrate that the second relaxation is sufficient to solve a general 7DOF IK problem. Our approach is certifiably globally optimal. We demonstrate the method on the 7DOF KUKA LBR IIWA manipulator and show that we are, in practice, able to compute the optimal IK or certify infeasibility in 99.9% tested poses. We also demonstrate that by the same approach, we are able to solve the IK problem for any generic (random) manipulator with seven revolute joints.