Dusko Katic

Survey of Intelligent Control Techniques for Humanoid Robots

This paper focusses on the application of intelligent control techniques (neural networks, fuzzy logic and genetic algorithms) and their hybrid forms (neuro-fuzzy networks, neuro-genetic and fuzzy-genetic algorithms) in the area of humanoid robotic systems. It represents an attempt to cover the basic principles and concepts of intelligent control in humanoid robotics, with an outline of a number of recent algorithms used in advanced control of humanoid robots. Overall, this survey covers a broad selection of examples that will serve to demonstrate the advantages and disadvantages of the application of intelligent control techniques.

A neural network-based classification of environment dynamics models for compliant control of manipulation robots

In this paper, a new method for selecting the appropriate compliance control parameters for robot machining tasks based on connectionist classification of unknown dynamic environments, is proposed. The method classifies the type of environment by using multilayer perceptron, and then, determines the control parameters for compliance control using the estimated characteristics. An important feature is that the process of pattern association can work in an on-line mode as a part of selected compliance control algorithm. Convergence process is improved by using evolutionary approach (genetic algorithms) in order to choose the optimal topology of the proposed multilayer perceptron. Compliant motion simulation experiments with robotic arm placed in contact with dynamic environment, described by the stiffness model and by the general impedance model, have been performed in order to verify the proposed approach.

SURVEY OF INTELLIGENT CONTROL ALGORITHMS FOR HUMANOID ROBOTS

Abstract This paper focusses on the application of intelligent control techniques (neural networks, fuzzy logic and genetic algorithms) and their hybrid forms (neuro-fuzzy networks, neuro-genetic and fuzzy-genetic algorithms) in the area of humanoid robotic systems. Overall, this survey covers a broad selection of examples that will serve to demonstrate the advantages and disadvantages of the application of intelligent control techniques.

Intelligent Control of Robotic Systems

1. Intelligent Control in Contemporary Robotics.- 2. Neural Network Approach in Robotics.- 3. Fuzzy Logic Approach in Robotics.- 4. Genetic Algorithms in Robotics.- 5. Hybrid Intelligent Approaches in Robotics.- 6. Synthesis of Connectionist Control Algorithms for Robot Contact Tasks.- 7. Synthesis of Comprehensive Connectionist Control Algorithms for Contact Tasks.- 8. Examples of Intelligent Techniques for Robotic Applications.- References.- About the Authors.

Stabilizing position/force control of robots interacting with environment by learning connectionist structures

Abstract This paper is mostly concerned with the application of connectionist architectures for fast on-line learning of robot dynamic uncertainties used at the executive hierarchical control level in robot contact tasks. The connectionist structures are integrated in non-learning control laws for contact tasks which enable stabilization and good tracking performance of position and force. It has been shown that the problem of tracking a specified reference trajectory and specified force profile with a present quality of their transient response can be efficiently solved by means of the application of a four-layer perceptron. A four-layer perceptron is part of a hybrid learning control algorithm through the process of synchronous training which uses fast learning rules and available sensor information in order to improve robotic performance progressively in the minimum possible number of learning epochs. Some simulation results of the deburring process with robot MANUTEC r3 are shown to verify effectiveness of the proposed control learning algorithms.

Learning impedance control of manipulation robots by feedforward connectionist structures

A major objective in this paper is the application of new connectionist structures for fast and robust online learning of internal robot dynamic relations used as part of impedance control strategies in the case of robot contact tasks. Using proposed connectionist structures, stabilization of robot motion and interaction force with environment is achieved. The proposed neural network models with their special topology are integrated in position-based impedance control, force-based impedance control and stabilizing impedance control. In this way, efficient dynamic compensation and fast learning properties of the control algorithm for contact tasks are enabled. The effectiveness of the learning method is shown by simulation experiments of robot deburring process.<<ETX>>

Connectionist approaches to the control of manipulation robots at the executive hierarchical level: An overview

One of the most interesting and important properties of connectionist systems is their ability to control sophisticated manipulation robots, i.e. to produce a large number of efficient control commands in real-time. This paper represents an attempt to give a comprehensive report of the basic principles and concepts of connectionism in robotics, with an outline of a number of recent algorithms used in learning control of a manipulation robot. A major concern in this paper is the application of neural networks for off-line and on-line learning of kinematic and dynamic relations used in robot control at the executive hierarchical level.

Robot compliance control algorithm based on neural network classification and learning of robot-environment dynamic models

In this paper, a new learning control algorithm based on neural network classification of unknown dynamic environment models and neural network learning of robot dynamic model is proposed. The method classifies characteristics of environments by using multilayer perceptrons, and then determines the control parameters for compliance control using the estimated characteristics. Simultaneously, using the second neural network the compensation of robot dynamic model uncertainties is accomplished. The classification capability of neural classifier is realized by efficient online training process. It is an important feature that the process of pattern classification can work in an online manner as a part of selected compliance control algorithm. Compliant motion simulation experiments have been performed in order to verify the proposed approach.

Decomposed connectionist architecture for fast and robust learning of robot dynamics

The application of connectionist architectures for fast and robust online learning of dynamic relations used in robot control at the executive hierarchical level is discussed. The proposed connectionist robot controllers use decomposition of robot dynamics. This method enables the training of neural networks on the simpler input/output relations with sigfnificant reduction of learning time. The other important features of these algorithms are fast and robust convergence properties because the problem of adjusting the weights of internal hidden units is considered as a problem of estimating parameters by the recursive least squares method and the extended Kalman filter approach. From simulation examples of robot trajectory tracking it is shows that when a sufficiently trained network is desired, the learning speed of the proposed algorithm is faster than that of the traditional backpropagation algorithms.<<ETX>>

Intelligent Control in Contemporary Robotics

Modern technological systems are characterized by poor system and subsystem models, high dimensionality of the decision space, distributed sensors and decision makers, high noise levels, multiple subsystems, levels, timescales and/or performance criteria, complex information patterns, overwhelming amount of data and stringent performance requirements. Hence, contemporary research in technological systems is oriented towards multi-disciplinary studies based on the synthesis and application of various control and management paradigms needed for efficient realization of the complex technological system goals and requirements and coping at the same time with all the mentioned problems and constraints.