Vassilios D. Tourassis

Discrete dynamic robot models

An inherently discrete-time dynamic model is introduced for robotic manipulators. Although robot dynamics are highly coupled and nonlinear, the model is compact and suitable for control engineering applications. The model is designed to guarantee conservation of energy (and momentum, if appropriate) at each sampling instant. Initial numerical experiments with cylindrical robots confirm the feasibility and applicability of the discrete dynamic robot model.

Properties and structure of dynamic robot models for control engineering applications

Abstract Controller design for robotic manipulators requires a fundamental physical understanding of the properties and structure of dynamic robot models. This paper focuses on the Lagrangian formulation which is attractive from both the dynamic modeling and control engineering points-of-view. Physical and mathematical properties and structural characteristics of the complete dynamic robot model are demonstrated. Implications of the model for control system analysis and design are then indicated. Physical interpretation leads naturally to the decomposition of the model into the positioning arm and end-effector subsystems and motivates the application of decentralized control to robotic manipulators. The authors then propose the application of control the positioning arm and artificial intelligence and intelligent sensors to control the end-effector.

The inertial characteristics of dynamic robot models

Abstract Robot dynamics are embedded in the mathematical foundations of classical mechanics to introduce novel physical interpretations and structural characteristics of the Lagrangian dynamic robot model. Within this framework, the centrality of the inertial matrix emerges. The physical significance of the inertial coefficients is further illuminated by the introduction of the coefficient of coupling of robotic manipulators. The properties of the inertial matrix follow directly from the kinematic and dynamic parameters of the robot. These properties translate into the characteristics of the centrifugal, Coriolis and gravitational components of the dynamic robot model. The novel approach reinforces the need to integrate the mechanical and controller designs of robotic manipulators. The conceptual framework leads to design guidelines for simplifying and reducing the nonlinear kinematic and dynamic coupling of robot dynamics. The development of the paper is applied to illustrate the properties and structural characteristics of industrial robots.

Towards Hebbian learning of Fuzzy Cognitive Maps in pattern classification problems

A detailed comparative analysis of the Hebbian-like learning algorithms applied to train Fuzzy Cognitive Maps (FCMs) operating as pattern classifiers, is presented in this paper. These algorithms aim to find appropriate weights between the concepts of the FCM classifier so it equilibrates to a desired state (class mapping). For these purposes, six different types of Hebbian learning algorithms from the literature have been selected and studied in this work. Along with the theoretical description of these algorithms and the analysis of their performance in classifying known patterns, a sensitivity analysis of the applied classification scheme, regarding some configuration parameters have taken place. It is worth noting that the algorithms are studied in a comparative fashion, under common configurations for several benchmark pattern classification datasets, by resulting to useful conclusions about their training capabilities.

Singularities of Euler and Roll-Pitch-Yaw Representations

Euler and roll-pitch-yaw angles are routinely used to represent the orientation of rigid bodies in aerospace, navigation, and robotics because they minimize the dimensionality of the control problem.Both representations, however, introduce unwarranted mathematical singularities which are identified in this paper.Trajectory-tracking algorithms break down at singularities and cause loss of control. Since mathematical singularities do not reflect physical limitations of orientation, remedial measures can beimplemented in the controller.

A modular architecture for inverse robot kinematics

A modular architecture for general-purpose inverse robot kinematics is developed. The authors synthesize kinematic modules for the robot arm and wrist and develop computational blocks to describe their respective functions. They then present an analytical framework that defines the inverse kinematic problem in terms of the proper coordination of the kinematic modules to accomplish the desired robot task. In this general-purpose framework, the inverse kinematics problem is always solvable in the feasible regions of the robot workspace, irrespective of whether the solution is analytically tractable. The modular architecture is based upon a nonlinear equation solver for which the Banach fixed-point theorem provides the theoretical basis. The proposed framework allows for the mathematical definition of the region in the robot workspace where convergence to the correct solution is guaranteed. It is insensitive to the initial estimates and provides for the computation of multiple solutions. >

Principles and design of model-based robot controllers

Model-based control algorithms for industrial manipulators require the on-line evaluation of robot dynamics and are particularly sensitive to modelling errors. The development of a unifying framework for the analysis and design of model-based robot control strategies is the theme of this paper. In this framework, the practical problems associated with real-time implementation are highlighted and methods to improve the robustness of the closed-loop system are suggested.

A Unified Methodology for Computing Accurate Quaternion Color Moments and Moment Invariants

In this paper, a general framework for computing accurate quaternion color moments and their corresponding invariants is proposed. The proposed unified scheme arose by studying the characteristics of different orthogonal polynomials. These polynomials are used as kernels in order to form moments, the invariants of which can easily be derived. The resulted scheme permits the usage of any polynomial-like kernel in a unified and consistent way. The resulted moments and moment invariants demonstrate robustness to noisy conditions and high discriminative power. Additionally, in the case of continuous moments, accurate computations take place to avoid approximation errors. Based on this general methodology, the quaternion Tchebichef, Krawtchouk, Dual Hahn, Legendre, orthogonal Fourier-Mellin, pseudo Zernike and Zernike color moments, and their corresponding invariants are introduced. A selected paradigm presents the reconstruction capability of each moment family, whereas proper classification scenarios evaluate the performance of color moment invariants.

Robust discrete nonlinear feedback control for robotic manipulators

Nonlinear feedback control algorithms for manipulators utilize a complete (coupled and nonlinear) dynamic robot model to decouple the robot joints. In the companion article1 we outlined the practical problems introduced by modeling inaccuracies, unmodeled dynamics and parameter errors. We then introduced the α-computed-torque nonlinear feedback control algorithm which is robust in the presence of the aforementioned error sources. In this article, we apply the insight gained from the α-computed-torque algorithm to design a robust discrete-time (accelerometer-free) computed-torque robot-control algorithm founded upon our discrete dynamic robot model.2 Numerical experiments with the cylindrical robot confirm both the robust disturbance rejection characteristics and the practical applicability of our discrete-time computer-torque control algorithm.

Inverse dynamics applications of discrete robot models

The authors highlight inverse dynamics applications of their recently introduced discrete-time dynamic robot model. The model is designed to guarantee conservation of energy (and momentum, if appropriate) over each sampling period, is accelerometer-free, and can reduce the computational time requirements of the real-time controller. Simulation experiments with three-degree-of-freedom cylindrical and articulated robots confirm the efficacy of the approach for inverse dynamics and trajectory planning applications.