Serial Robot Manipulators Research Articles

Study of Automatic Fiber Placement Manipulator's Robotic Kinematics Manipulability Based on Volume Element

The method is proposed based on volume element in order to measure the manipulator’s robotic kinematics manipulability. Then studied the series redundant automatic fiber placement robotic manipulator’s operation space, draw the conclusion that the greater of the robotic manipulator’s operation space volume, the better of the robotic manipulator’s manipulability, volume element based on redundant robotic manipulator’s kinematics is proposed as an operational performance index. n-DOF serial robotic manipulator’s operation space is n-dimensional Riemannian manifold, the n-dimensional Riemannian manifold volume is calculated using the moving coordinate system and the exterior product definition in differential geometry and get the robotic manipulator’s operation space volume then compared the obtained results with the operation space volume using inner product determinant in the literature, it shows that the volume element as a kinematics operational performance index is feasible.

Position Control of a Four Link Hyper Redundant Robotic Manipulator

Hyper-Redundant robotic manipulators like Serial robots, Snake robots, Tentacle robots or Continuum robots have very large number of degrees of kinematics redundancy. Position control of such a robotic manipulator comprising of more than three links and articulated joints is a big challenge due to the involvement of large number of trigonometric terms in its inverse kinematics equations. In this paper, a simple algorithm for the inverse kinematics solution (IKS) of a four-link serial robotic manipulator has been proposed, which is then validated experimentally. The proposed method divides the robot into two two-link virtual sub-robot and solves the inverse kinematics analytically for joint variables of each sub-robot successively. A validation experiment was conducted on a 4-link prototype to check the validity of the proposed algorithm. The experimental results showed satisfactory results with good repeatability.

Nonlinear disturbance observer design for robotic manipulators

Robotic manipulators are highly nonlinear and coupled systems that are subject to different types of disturbances such as joint frictions, unknown payloads, varying contact points, and unmodeled dynamics. These disturbances, when unaccounted for, adversely affect the performance of the manipulator. Employing a disturbance observer is a common method to reject such disturbances. In addition to disturbance rejection, disturbance observers can be used in force control applications. Recently, research has been done regarding the design of nonlinear disturbance observers (NLDOs) for robotic manipulators. In spite of good results in terms of disturbance tracking, the previously designed nonlinear disturbance observers can merely be used for planar serial manipulators with revolute joints [Chen, W. H., Ballance, D. J., Gawthorp, P. J., O'Reilly, J. (2000). A nonlinear disturbance observer for robotic manipulators. IEEE Transactions on Industrial Electronics, 47 (August (4)), 932–938; Nikoobin, A., Haghighi, R. (2009). Lyapunov-based nonlinear disturbance observer for serial n-link manipulators. Journal of Intelligent & Robotic Systems, 55 (July (2–3)), 135–153]. In this paper, a general systematic approach is proposed to solve the disturbance observer design problem for robotic manipulators without restrictions on the number of degrees-of-freedom (DOFs), the types of joints, or the manipulator configuration. Moreover, this design method does not need the exact dynamic model of the serial robotic manipulator. This method also unifies the previously proposed linear and nonlinear disturbance observers in a general framework. Simulations are presented for a 4-DOF SCARA manipulator to show the effectiveness of the proposed disturbance observer design method. Experimental results using a PHANToM Omni haptic device further illustrate the effectiveness of the design method.

Simulation tool for teaching and learning 3D kinematics workspaces of serial robotic arms with up to 5‐DOF

AbstractThis work develops a new educational simulation tool whose objective is to make attractive and practical teaching and learning in kinematics of serial robotic arms. At engineering laboratories in which practices with robotic arms are necessary for understanding fundamental concepts of theory, students need to accomplish a complete study attending to different aspects related with kinematics. Its graphic user interface (GUI) is a straight‐forward and easy‐to‐follow Denavit–Hartenberg (DH)‐based method which helps at engineering practices without requiring a programming knowledge. We develop, in a theoretical way, both forward (FK) and inverse kinematics (IK), which are necessary to demonstrate the study of an educational robotic arm as a practical case. The interactive simulation tool allows defining DH parameters and geometry in serial arms with up to 5 degrees of freedom (DOF). Thus, the interest of this work lies in an innovative graphic environment which teaching professionals can use to study DH convention, observe the movement of serial robotic manipulators and map both forward and joint workspaces in a visual manner. This process is much simpler that interpreting the results in an analytic way. Furthermore, the generic capabilities of this simulation tool allow comparing different robot arm configurations (considering its physical parameters and mechanical characteristics previously known), both if robotic arms are physically available or not in lab. © 2010 Wiley Periodicals, Inc. Comput Appl Eng Educ 20: 750–761, 2012

Inverse Kinematics Analysis of 5-DOF Robot Manipulators Based on Virtual Joint Method

To solve the inverse kinematics problem of a robot manipulator without closed form solutions, one-dimensional iterative method is very useful. However, for a 5-DOF robot manipulator, because of the uncontrolable and uncertain orientation vectors, it's difficult to analytically express all joint variables by one of them, therefore one-dimensional iterative method can not be directedly used. By adding an appropriate virtual joint to it, a 5-DOF manipulator can be changed into a 6-DOF one so that the uncertain orientation vectors can be pre-given, and the difficulty is solved. To illustrate this virtual joint method a 5-DOF serial robot manipulator with prismatic arm joint and offset wrist is discussed in this paper as an example.

Performance Prediction Network for Serial Manipulators Inverse Kinematics Solution Passing Through Singular Configurations

This paper is devoted to the application of Artificial Neural Networks (ANN) to the solution of the Inverse Kinematics (IK) problem for serial robot manipulators, in this study two networks were trained and compared to examine the effect of considering the Jacobian Matrix to the efficiency of the IK solution.Given the desired trajectory of the end effector of the manipulator in a free-of-obstacles workspace, Offline smooth geometric paths in the joint space of the manipulator are obtained. Even though it is very difficult in practice, data used in this study were recorded experimentally from sensors fixed on robot's joints to overcome the effect of kinematics uncertainties presence in the real world such as ill-defined linkage parameters, links flexibility and backlashes in gear trainThe generality and efficiency of the proposed algorithm are demonstrated through simulations of a general six DOF serial robot manipulator, finally the obtained results have been verified experimentally.

Optimal Task Placement of a Serial Robot Manipulator for Manipulability and Mechanical Power Optimization

Power consumption and accuracy are main aspects to be taken into account in the movement executed by high performance robots. The first aspect is important from the economical point of view, while the second is requested to satisfy technical specifications. Aiming at increasing the robot performance, a strategy that maximizes the manipulator accuracy and minimizes the mechanical power consumption is considered in this work. The end-effector is constrained to follow a predefined path during the optimal task positioning. The proposed strategy defines a relation between mechanical power and manipulability as a key element of the manipulator analysis, establishing a performance index for a rigid body transformation. This transformation is used to compute the optimal task positioning through the optimization of a multicriteria objective function. Numerical simulations regarding a serial robot manipulator demonstrate the viability of the proposed methodology.

An adaptive niching genetic algorithm approach for generating multiple solutions of serial manipulator inverse kinematics with applications to modular robots

SUMMARYInverse kinematics (IK) is a nonlinear problem that may have multiple solutions. A modified genetic algorithm (GA) for solving the IK of a serial robotic manipulator is presented. The algorithm is capable of finding multiple solutions of the IK through niching methods. Despite the fact that the number and position of solutions in the search space depends on the position and orientation of the end-effector as well as the kinematic configuration (KC) of the robot, the number of GA parameters that must be set by a user are limited to a minimum through the use of an adaptive niching method. The only requirement of the algorithm is the forward kinematics (FK) equations which can be easily obtained from the Denavit–Hartenberg link parameters and joint variables of the robot. For identifying and processing the outputs of the proposed GA, a modified filtering and clustering phase is also added to the algorithm. For the postprocessing stage, a numerical IK solver is used to achieve convergence to the desired accuracy. The algorithm is validated on three KCs of a modular and reconfigurable robot (MRR).

An off‐line robot simulation toolbox

AbstractRobotics has gained popularity in education so many engineering schools are offering robotics courses. In this study, a novel robot toolbox, “ROBOLAB” is developed for the educational users to improve the understanding of robotics fundamentals through interactive real‐time simulation. To understand manipulator movement in 3‐D space with increasing number of joints is very difficult for engineering students; because the mathematical model between joint space and physical space becomes more complex. In order to overcome this complexity, ROBOLAB based on MATLAB Graphical User Interface (GUI) includes a library for the 16 different 6 degree of freedom (6‐DOF) fundamental serial robot manipulators. The user has option to view an animation of the robot manipulators with choosing one of them or to create own GUIs based on robot project. ROBOLAB provides valuable analysis tools to students and engineering professionals in which they can compute rotation and transformation matrix, forward and inverse kinematics, and trajectory planning. Additionally, it allows user to use powerful MATLAB features such as controlling over data and formatting. In order to illustrate the features of ROBOLAB, RS cylindrical robot manipulator is given here as an example. Usage of real industrial robots in laboratory may be a very expensive way to teach robotics courses. ROBOLAB may help students to accomplish the courses and projects involving robots with minimum cost and to see 3‐D space robot applications more effectively. © 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 18: 41–52, 2010; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae.20236

Actuator Gain Distributions to Analytically Meet Specified Performance Capabilities in Serial Robot Manipulators

A serial robotic manipulator arm is a complex electromechanical system whose performance is characterized by its actuators. The actuator itself is a complex nonlinear system whose performance can be characterized by the speed and torque capabilities of its motor, and its accuracy depends on the resolution of the encoder as well as its ability to resist deformations under load. The mechanical gain associated with the transmission is critical to the overall performance of the actuator since it amplifies the motor torque, thus improving the force capability of the manipulator housing it, reduces the motor speed to a suitable output speed operating range, and amplifies the stiffness improving the precision under load of the overall system. In this work, a basic analytic process that can be used to manage the actuator gain parameter to obtain an improved arm design based on a set of desired/required performance specifications will be laid out. Key to this analytic process is the mapping of the actuator parameters (speed, torque, stiffness, and encoder resolution) to their effective values at the system output via the mechanical gains of the actuators as well as the effective mechanical gains of the manipulator. This forward mapping of the actuator parameters allows the designer to determine how each of the parameters influences the functional capacity of the serial manipulator arm. The actuator gains are then distributed along the effective length of the manipulator to determine their effects on the performance capabilities of the system. The analytic formulation is also demonstrated to be effective in addressing the issue of configuration management of serial robotic manipulators where the goal is to assemble a system that meets some required performance specifications. To this end, two examples demonstrating a solution of the configuration management problem are presented. The analytic process developed based on the mapping of the mechanical parameters of the actuator to their effective values at the system output is shown to dramatically reduce the effort in the initial phases of the design process, meaning that the number of design iterations can be dramatically reduced.

Trajectory tracking for a serial robot manipulator passing through singular configurations based on the adaptive kinematics Jacobian method

This paper discusses the use of artificial neural networks (ANNs) as a method of trajectory tracking control for a robotic system. Using an ANN does not require any prior knowledge of the kinematics model of the system being controlled; the basic idea of this concept is the use of the ANN to learn the characteristics of the robot system rather than to specify an explicit robot system model. In this approach, disadvantages of some schemes such as the fuzzy learning control, for example, have been elevated. Off-line training was performed for a geometric trajectory that is free of obstacles. Studying the kinematics Jacobian of serial manipulators by using ANNs has two problems: one of these is the selection of the appropriate configuration of the network and the other is the generation of a suitable training dataset. In this approach, although this is very difficult in practice, training data were recorded experimentally from sensors fixed on each joint to overcome the effect of kinematics uncertainties present in the real world such as ill-defined linkage parameters, links flexibility, and backlashes in the gear train. Then two network configurations were compared to find the best configuration to be used. Finally, the simulation results were verified experimentally using a general six-degree-of-freedom (DOF) serial robot manipulator.

Uniform Bounds of the Coriolis/Centripetal Matrix of Serial Robot Manipulators

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Many control laws for robot manipulators assume uniform boundedness of the Coriolis/centripetal matrix to insure global stability. In this paper, a new procedure to derive explicitly uniform bounds of the Coriolis/centripetal matrix is proposed. This procedure has the advantage with respect to other methods of avoiding the computation of the Christoffel symbols of first order involved in the derivation of the dynamics of the whole mechanism. To this end a new description of the Coriolis/centripetal matrix has been introduced based on fundamental matrices which exhibit structural properties of commutation and representation. </para>

Robot path planning in a constrained workspace by using optimal control techniques

Robot manipulators are programmable mechanical systems designed to execute a great variety of tasks in a repetitive way. In industrial environment, while productivity increases, cost reduction associated with robotic operation and maintenance can be obtained as a result of decreasing the values of dynamic quantities such as torque and jerk, with respect to a specific task. Furthermore, this procedure allows the execution of various tasks that require maximum system performance. By including obstacle avoidance ability to the robot skills, it is possible to improve the robot versatility, i.e., the robot can be used in a variety of operating conditions. In the present contribution, a study concerning the dynamic characteristics of serial robot manipulators is presented. An optimization strategy that considers the obstacle avoidance ability together with the dynamic performance associated with the movement of the robot is proposed. It results an optimal path planning strategy for a serial manipulator over time varying constraints in the robot workspace. This is achieved by using multicriteria optimization methods and optimal control techniques. Numerical simulation results illustrate the interest of the proposed methodology and the present techniques can be useful for the design of robot controllers.

A new adaptive learning algorithm for robot manipulator control

This paper is devoted to the development and implementation of neural network technology to solve the inverse kinematics problems for serial robot manipulators, given the desired Cartesian path of the end effector of the manipulator in a free-of-obstacles workspace. Offline smooth geometric paths in the joint space of the manipulator are obtained. The proposed technique does not require any prior knowledge of the kinematics model of the system being controlled; the main idea of this approach is the use of an artificial neural network to learn the robot system characteristics rather than having to specify an explicit robot system model. Since one of the most important problems in using artificial neural networks is the choice of the appropriate network configuration, two different configurations were compared; they were trained to learn the desired set of joint angles positions from a given set of end effector positions. The generality and efficiency of the proposed algorithm are demonstrated through simulations of a general six-degrees-of-freedom serial robot manipulator.

Application of robot kinematics methods to the simulation and control of neutron beam line positioning systems

Neutron stress measurements require specimens of complex geometry to be speedily and accurately positioned and oriented with respect to the neutron beam. Recognition that a majority of the specimen positioning systems in use at strain scanning facilities are effectively serial robot manipulators, suggests that the methods of serial robot kinematic modelling may be applied to advantage. The adoption of robotics methods provides a simple and reliable framework for controlling positioning systems of arbitrary geometry and complexity. In addition the numerical solution of the inverse kinematic problem is facilitated, allowing specimens to be automatically positioned and orientated so that pre-determined strain components are measured. It is also shown that, given sufficient degrees of freedom, a secondary characteristic of the measurement position such as the measurement count time may be simultaneously optimised.

Autonomous robot calibration using vision technology

Unlike the traditional robot calibration methods, which need external expensive calibration apparatus and elaborate setups to measure the 3D feature points in the reference frame, a vision-based self-calibration method for a serial robot manipulator, which only requires a ground-truth scale in the reference frame, is proposed in this paper. The proposed algorithm assumes that the camera is rigidly attached to the robot end-effector, which makes it possible to obtain the pose of the manipulator with the pose of the camera. By designing a manipulator movement trajectory, the camera poses can be estimated up to a scale factor at each configuration with the factorization method, where a nonlinear least-square algorithm is applied to improve its robustness. An efficient approach is proposed to estimate this scale factor. The great advantage of this self-calibration method is that only image sequences of a calibration object and a ground-truth length are needed, which makes the robot calibration procedure more autonomous in a dynamic manufacturing environment. Simulations and experimental studies on a PUMA 560 robot reveal the convenience and effectiveness of the proposed robot self-calibration approach.

Invariant Measures of Closeness to Linear Dependency of Screw Systems

This paper describes a new, general analytical method of determining the closeness to linear dependence of any system of lines and screws, giving n invariant scalars for an n-system of screws. The method proposed evaluates the maximum available scalar or Klein product between the unit screw under test and any unit combination of screws reciprocal to all of the set except the test screw. Examples are given of the application of this theory to the behaviour of a partially complete system of kinematic restraint such as those used in manufacturing fixtures of a simple structure and of a serial robotic manipulator.

An adaptive-learning algorithm to solve the inverse kinematics problem of a 6 D.O.F serial robot manipulator

An adoptive learning strategy using an artificial neural network ANN has been proposed here to control the motion of a 6 D.O.F manipulator robot and to overcome the inverse kinematics problem, which are mainly singularities and uncertainties in arm configurations. In this approach a network have been trained to learn a desired set of joint angles positions from a given set of end effector positions, experimental results has shown an excellent mapping over the working area of the robot, to validate the ability of the designed network to make prediction and well generalization for any set of data, a new training using different data set has been performed using the same network, experimental results has shown a good generalization for the new data sets. The proposed control technique does not require any prior knowledge of the kinematics model of the system being controlled, the basic idea of this concept is the use of the ANN to learn the characteristics of the robot system rather than to specify explicit robot system model. Any modification in the physical set-up of the robot such as the addition of a new tool would only require training for a new path without the need for any major system software modification, which is a significant advantage of using neural network technology.

Smart Actuator Positioning and Displacement Transmissibility in Serial and Parallel Robot Manipulators for Performance Enhancement

A method is presented for the evaluation of the transmissibility of displacement from smart (active) actuators integrated in the structure of robot manipulators to the manipulator joint and end-effector displacements. The method is based on studying the characteristics of the Jacobian of the mapping function between the two displacements for a given position of the robot manipulator. The developed method provides a tool for the determination of the positioning of smart actuators to provide maximum effectiveness in eliminating high harmonics of the joint or the end-effector motion. In robots with serial and parallel kinematics chains containing nonprismatic joints, due to the associated kinematics nonlinearity, if the joint motions were synthesized with low harmonic trajectories, the end-effector trajectory would still contain high harmonics of the joint motions. Alternatively, if the end-effector motion were synthesized with low harmonic components, due to the inverse kinematics nonlinearity, the actuated joint trajectories would contain a significant high harmonic component. As a result, the operating speed and tracking precision are degraded. By integrating smart materials based actuators in the structure of robot manipulators to provide small amplitude and high frequency motions, the high harmonic component of the actuated joint and/or the end-effector motions can be significantly reduced, thereby making it possible to achieve higher operating speed and tracking precision.

Robots and Screw Theory: Applications of Kinematics and Statics to Robotics

<i>Robots and Screw Theory: Applications of Kinematics and Statics to Robotics</i>