Simulating the inverse kinematic model of a robot through artificial neural networks:complementing the teaching of robotics

Teaching robotics necessarily involves the study of the kinematic models of robot manipulators. In turn, the kinematics of a robot manipulator can be described by its forward and reverse models. The inverse kinematic model, which provides the status of the joints according to the desired position for the tool of the robot, is typically taught and described in robotics classes through an algebraic way. However, the algebraic representation of this model is often difficult to obtain. Thus, although it is unquestionable the need for the accurate determination of the inverse kinematic model of a robot, the use of ANNs (artificial neural networks) in the design stage can be very attractive, because it allows us to predict the behavior of the robot before the formal development of its model. In this way, this paper presents a relatively quick way to simulate the inverse kinematic model of a robot, thereby allowing the student to have an overview of the model, coming to identify points that should be corrected, or that can be optimized in the structure of a robot496096

Simulation of robots' inverse kinematics in engineering education: an approach based on genetic algorithms

The study of articulated robots necessarily goes through the development of their kinematic models. In turn, the kinematics of a robot can be described through its direct and inverse models. The inverse kinematic model, through which the state of the joints is obtained as a function of the desired position for the free end of the robot, is usually described algebraically. However, this representation is often difficult to obtain. Thus, while the exact determination of the inverse kinematic model is unquestionable, the use of genetic algorithms in the design stage can be very attractive because it allows predicting the behavior of the robot before the formal development of its model. In this sense, the results of this work present a relatively fast way to simulate the inverse kinematic model, which can be useful in teaching robotics in engineering, allowing the student to have a broader view of the model, coming to identify points that must be corrected or that can be optimized in the structure of a robot. It can be concluded that the use of genetic algorithms in robotics is feasible, having as main advantages its easy computational implementation and its precision in the representation of kinematic models381556

Self-Motions of General 3-RPR Planar Parallel Robots

This paper studies the kinematic geometry of general 3-RPR planar parallel robots with actuated base joints. These robots, while largely overlooked, have simple direct kinematics and large singularity-free workspace. Furthermore, their kinematic geometry is the same as that of a newly developed parallel robot with SCARA-type motions. Starting from the direct and inverse kinematic model, the expressions for the singularity loci of 3-RPR planar parallel robots are determined. Then, the global behaviour at all singularities is geometrically described by studying the degeneracy of the direct kinematic model. Special cases of self-motions are then examined and the degree of freedom gained in such special configurations is kinematically interpreted. Finally, a practical example is discussed and experimental validations performed on an actual robot prototype are presented

An autonomous image-guided robotic system simulating industrial applications

This paper presents a robotic system based on a serial manipulator. The robot is a vertical articulated arm with 5 revolute joints having 6 Degree Of Freedom. Actuated with six precise servo motors, the system offers positional accuracy of ±0.5mm with a movement speed of 100mm/s. Forward and Inverse Kinematic model of the robot has been developed and its workspace has been analyzed to facilitate the use of robotic arm as a simulated industrial manipulator. Image processing has been done to make system more autonomous. Followed by a user's commands, the system acquires image of the environment using on-board camera. This image is processed to extract information about object's coordinates. Based on these coordinates, Inverse Kinematic model computes the required joint angles for the end-effector to reach at desired position and orientation thus enabling it to manipulate the object. The proposed system can be used in wide range of industrial applications involving pick and place, sorting and other object manipulation tasks. The system can also be potentially useful for heavy and 'giant' industrial applications after scaling up i.e. using huge robotic arm, employing multiple and better cameras and optimizing algorithms. © 2012 IEEE

Automatic selection of the Groebner Basis' monomial order employed for the synthesis of the inverse kinematic model of non-redundant open-chain robotic systems

This is an Author's Accepted Manuscript of an article published in José Guzmán-Giménez, Ángel Valera Fernández, Vicente Mata Amela & Miguel Ángel Díaz-Rodríguez (2023) Automatic selection of the Groebner Basis¿ monomial order employed for the synthesis of the inverse kinematic model of non-redundant open-chain robotic systems, Mechanics Based Design of Structures and Machines, 51:5, 2458-2480, DOI: 10.1080/15397734.2021.1899829 [copyright Taylor & Francis], available online at: http://www.tandfonline.com/10.1080/15397734.2021.1899829[EN] The methods most commonly used to synthesize the Inverse Kinematic Model (IKM) of open-chain robotic systems strongly depend on the robot's geometry, which make them difficult to systematize. In a previous work we presented a systematic procedure that relies on Groebner Bases to synthesize the IKM of non-redundant open-chain robots. This study expands the developed procedure with a methodology for the automatic selection of the basis' monomial order. The procedure's inputs are the robot's Denavit-Hartenberg parameters and the movement range of its actuators, while the output is the synthesized IKM, ready to be used in the robot's control system or in a simulation of its behavior. This procedure can synthesize the IKM of a wide range of open-chain robotic systems, such as Cartesian robots, SCARA, non-redundant multi-legged robots, and all non-redundant manipulators that satisfy the in-line wrist condition. The procedure's performance is assessed through two study cases of open-chain robots: a walking hexapod and a PUMA manipulator. The optimal monomial order is successfully identified for all cases. Also the output errors of the synthesized IKMs are negligible when evaluated in their corresponding workspaces, while their computation times are comparable to those required by the kinematic models calculated by traditional methods.This research was partially funded by Plan Nacional de IthornDthorni, Agencia Estatal de Investigacion del Ministerio de Economia, Industria y Competitividad del Gobierno de Espana, in the project FEDER-CICYT DPI201784201-R.Guzmán-Giménez, J.; Valera Fernández, Á.; Mata Amela, V.; Díaz-Rodríguez, MÁ. (2023). Automatic selection of the Groebner Basis' monomial order employed for the synthesis of the inverse kinematic model of non-redundant open-chain robotic systems. Mechanics Based Design of Structures and Machines. 51(5):2458-2480. https://doi.org/10.1080/15397734.2021.18998292458248051

Generic Techniques for the Calibration of Robots with Application of the 3-D Fixtures and Statistical Technique on the PUMA 500 and ARID Robots

A relatively simple, inexpensive, and generic technique that could be used in both laboratories and some operation site environments is introduced at the Robotics Applications and Development Laboratory (RADL) at Kennedy Space Center (KSC). In addition, this report gives a detailed explanation of the set up procedure, data collection, and analysis using this new technique that was developed at the State University of New York at Farmingdale. The technique was used to evaluate the repeatability, accuracy, and overshoot of the Unimate Industrial Robot, PUMA 500. The data were statistically analyzed to provide an insight into the performance of the systems and components of the robot. Also, the same technique was used to check the forward kinematics against the inverse kinematics of RADL's PUMA robot. Recommendations were made for RADL to use this technique for laboratory calibration of the currently existing robots such as the ASEA, high speed controller, Automated Radiator Inspection Device (ARID) etc. Also, recommendations were made to develop and establish other calibration techniques that will be more suitable for site calibration environment and robot certification

Robotic arm with ten degrees of freedom

ABSTRACT: This bachellor thesis present a matlab code that makes a robotic arm to work. Controlling the arm means that all the movements of the arm are defined. The robotic arm is ten degrees of freedom. Inside this task we are going to use the code of the optimization algorithm based on genetic algorithms and the knowledge of direct kinematics equations to get the inverse kinematics model used to control the movement of a robotic arm. With this combination we got a good inverse kinematic model of 10 D.O.F. robot mechanism.Grado en Ingeniería en Tecnologías Industriale

Towards learning inverse kinematics with a neural network based tracking controller

Learning an inverse kinematic model of a robot is a well studied subject. However, achieving this without information about the geometric characteristics of the robot is less investigated. In this work, a novel control approach is presented based on a recurrent neural network. Without any prior knowledge about the robot, this control strategy learns to control the iCub’s robot arm online by solving the inverse kinematic problem in its control region. Because of its exploration strategy the robot starts to learn by generating and observing random motor behavior. The modulation and generalization capabilities of this approach are investigated as well

Robust design for the lower extremity exoskeleton under a stochastic terrain by mimicking wolf pack behaviors

While kinematics analysis plays an important role in studying human limb motions, existing methods (namely, direct and inverse kinematics) have their deficiencies. To improve, this paper develops a robust design method using artificial intelligence and applies it to the lower extremity exoskeleton design under a stochastic terrain. An inverse kinematic model is first built considering the impact on human's comfort from the stochastic terrain. Then, a robust design model is constructed based on the inverse kinematic model, where the design framework mimics wolf pack behaviors and the robust design problem is thus solved for keeping probabilistic consistency between the exoskeleton and its wearer. A case study validates the effectiveness of the developed robust method and algorithm, which ensures walking comfort under the stochastic terrain within the validity of simulations

SYMORO+: A SYSTEM FOR THE SYMBOLIC MODELLING OF ROBOTS

International audienceThis paper presents the software package SYMORO+ for the automatic symbolic modelling of robots. This package permits to generate the direct geometric model, the inverse geometric model, the direct kinematic model, the inverse kinematic model, the dynamic model, and the inertial parameters identification models. The structure of the robots can be serial, tree structure or containing closed loops. The package runs on Sun stations and PC computers, it has been developed under MATHEMATICA and C language. In this paper we give an overview of the algorithms used in the different models, the computational cost of the dynamic models of the PUMA robot are given