Intelligent Systems and Robotics


In the recent years there has been growing interest in industrial systems and in particular in robotic manipulators and mobile robot systems. As the cost of robots goes down and as robots become more compact, the number of industrial applications of robotic systems increases. Moreover, there is need to design industrial systems with intelligence, autonomous decision making capabilities, and self-diagnosing properties.

The design of intelligent industrial systems requires the synergism of several research areas, such as robotics, control, estimation and sensor fusion, fault diagnosis, optimization and machine intelligence. Incorporating intelligence in industrial and mobile robots can help to increase productivity, cut-off production costs, and to improve working conditions and safety in industrial environments. This need has resulted in the rapid development of modeling and control methods for industrial systems and robots, of fault detection and isolation methods for the prevention of critical situations in industrial work-cells and production plants, of optimization methods aiming at a more profitable operation of industrial installations and of machine intelligence methods aiming at reducing human intervention in industrial systems operation.

Intelligence for industrial systems is characterized by two main features: (i) learning and (ii) uncertainty handling. Learning indicates the capability of the industrial system to adapt its behavior according to operating conditions and to accumulate information from sensor inputs so as to make its functioning more efficient. Learning can be succeeded by gradient-based algorithms (neural and adaptive fuzzy systems, automata networks and intelligent agents) or gradient-free algorithms (evolutionary and particle methods). Uncertainty handling is also important for successful operation of industrial systems since precise mathematical models are in several cases intractable or the modeling procedure is on its own too complicated. Stochastic modeling can be performed in terms of possibilistic or probabilistic models and stochastic estimation algorithms. 

Significant topics in Intelligent Systems and Robotics are: (i) industrial and mobile robotics, (ii) stochastic modeling for industrial systems, (iii) electric power systems, and (iv) fault diagnosis.

(i) In the area of industrial and mobile robotics one can consider various problems ranging from dynamic or kinematic modeling and control, to path and planning, sensor fusion and machine vision. The application fields mainly include (a) industrial robots designed for compliance tasks (such as deburring, milling, cutting and assembling), as  well as industrial robots designed for non-contact tasks (such as welding and painting), (b) mobile robots, autonomous vehicles and multi-robot systems designed for hazardous inspection, underwater or space exploration, transportation, search and rescue, and underground exploitation of energy resources. Some examples of defence applications are guarding, escorting, patrolling (surface surveillance), and strategic behaviours, such as stalking and attacking, (c) service robots for domestic use, assistance of the elderly, as well as medical robots. In all these applications robot intelligence is of primary importance for successful operation in varying and uncertain environments.

(ii) In the area of stochastic modeling for industrial systems one can consider (a) the problem of identification of a dynamical system’s model from numerical data which in turn consists of various sub-problems  such as selection of the structure and the order of the model, optimization of the model parameters, model simplification and model validation, (b) the problem of filtering and state estimation from measurements of the inputs and outputs of the dynamical system which enables sensorless control of fault diagnosis tasks. Identification and state estimation are critical for control and fault monitoring of industrial systems in real operating conditions. There can be nonparametric approximators such neural and wavelet networks or other basis functions expansions, fuzzy (possibilistic) models or probabilistic models such as Bayesian networks and stochastic automata, and stochastic filtering algorithms in terms of the Kalman Filter variants or particle filters.

(iii) In the area of electric power systems one can consider intelligent algorithms for improving the operation and the security of power generation and distribution systems.  Among the problems faced in this field one can distinguish (a) dynamic modeling of the power grid, forecasting of its behavior on short or long-term basis and optimization of its operation (b) adaptive compensation of undesirable power system characteristics and robust power system stabilization, (c) monitoring and fault diagnosis for the various components of the power generation transmission and distribution grid.

(iv) In the area of fault diagnosis one can consider various methods for fault detection where the aim is to recognize that a fault happened and fault isolation where the aim is to find the fault and the location of the fault. Fault diagnosis is applied to robotics and industrial production systems, transportation systems, the electric power grid, civil and mechanical structures etc. Advanced methods of fault detection are based on mathematical signal and process models and of methods based on systems theory and statistics or artificial intelligence to generate the fault symptoms. The monitored system can be represented either by a continuous/discrete time model or a discrete event model. Important issues in fault diagnosis of industrial systems, which are examined in this book, are identification of dynamical systems and residual generation, fault detection with state observers and state estimation, fault threshold selection and change detection criteria, fault diagnosis in the frequency domain, fault diagnosis with parity equations and pattern recognition methods.

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