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This paper (Part II) investigates the motion of a redundant anthropomorphic arm during the writing task. Two approaches are applied. The first is based on the concept of distributed positioning which is suitable to model the “writing” task before the occurrence of fatigue symptoms. The second approach uses the concept of “virtual fatigue” (VF) which is a variable that dynamically behaves in a way analogous to the biological fatigue. VF enables the arm to reconfigure itself and take postures appropriate for the current level of fatigue. The study includes the analysis of legibility and inclination of handwriting, and a set of simulation results that show most practical aspects of robot human-like performance.

Robotic soccer is an interesting test bench for the field of self-organizing and cooperating multi-agent systems. This paper deals with learning of two basic low-level behaviors that will enable the robotic player to participate further in higher-level collaborative and adversarial learning situations. First, a ball interception and obstacle avoidance behavior is learned. Then the acquired skills are incorporated into a next higher-level multi-agent learning scenario, namely the shooting ball behavior. The proposed control scheme for these behaviors consists of a trajectory generator with a layered structure, which supplies data to a trajectory-tracking controller.

Total Quality Management (TQM) principles and methods try to improve the overall performance of an enterprise or organization. Knowledge Based Systems (KBS) can support Total Quality Management procedures by organizing, storing and processing TQM knowledge and both rule-based and object-oriented knowledge representation structures and reasoning can be used for these applications. Three interesting cases concerning the application of KB systems in TQM will be presented in this paper: a)Statistical process quality control reasoning using knowledge based systems, b)Knowledge based decision support systems for quality circles, and c)Knowledge based Business Process Reengineering (BPR) and software design.

The rapid increase of warehouse use demands automated management services in order to make decisions for all tasks concerned. These decisions must ensure optimised usage of resources, which leads to cost reduction and better customer service. A major consideration is the way the warehouse area, which consists of different storage types and similar product groups, is exploited. The optimisation of the warehouse's occupied area is the target of replenishment, which iS essentially a constrained placement problem. In this paper, a genetic algorithm with revised operators iS developed. This algorithm is applied to real warehouse data and results show that it produces successful replenishments in a complex environment where many criteria have to be considered and met to some user-defined extent.

In this paper genetic algorithms (GAs) are applied to improve the performance of fuzzy and neural multivariable controllers. In the case of fuzzy multivariable controllers the GA task is to determine an appropriate compensation element which will per–form partial decoupling between system inputs and outputs thus permitting the application of a decentralized control scheme. A novel robust fuzzy logic controller is then applied to each one of the SISO subsystems. The neural multivariable controller is based on the Gawthrop-type MIMO controller but now a neural network is used for the prediction of the system outputs. A genetic algorithm decides how much the control signal should contribute in the cost function thus succeeding the optimal compromise between system stability and zero steady-state error.

Fuzzy relational systems can represent symbolic knowledge in a formal numerical (subsymbolic) framework, with the aid of fuzzy relation equations. The disadvantage of this methodology is the need for a priori knowledge in order to construct the fuzzy relation equation. In this paper, a neural network model is proposed in order to represent fuzzy relational systems without the need of the construction of the fuzzy relation equation. The network ensures the ideal perfect recall of fuzzy associative memories when the a posteriori constructed fuzzy relation equation has a non-empty solution set. It is actually a single layer of generalized neurons (compositional neurons) that perform the sup-t-norm composition. An on-line learning algorithm adapting the weights of its interconnections is incorporated into the neural network. These weights are actually the elements of the fuzzy relation representing the fuzzy relational system. The algorithm is based on the knowledge about the topographic structure of the respective fuzzy relation.

This paper is concerned with the solution of the principal component analysis (PCA) problem with the aid of neural networks (NNs). After an overview of the basic NN-based PCA concepts and a listing of the available algorithms, two criteria for evaluating PCA NN algorithms are proposed. Then, a new criterion for the generation of improved PCA NN structures with reduced size is presented. Using this criterion, one can start with a small network and dynamically add new nodes at the hidden layer(s) during training, one at a time, until the desired performance is achieved. A simulation example is provided that shows the applicability and effectiveness of the methodology.

This paper is concerned with the trajectory definition in robot tasks. Although very often ignored, the specification of robot motion is not the first step in the definition of a robot task. The task definition starts with the description of the final outcome, i.e. with the specification of the job to be performed. When this is done, the proper robot kinematics (motion law) is defined. This step, from the outcome of the task to the robot motion, is sometimes straightforward and dictated by the technology applied. However, even if a multiple choice of motion is possible leading to the same outcome, this possibility is usually avoided by preassuming a certain optimality criterion for the quality of work. Clearly, such an approach does not leave the possibility for some additional optimization in the sense of a secondary objective. So, some potential benefits are lost. This paper starts from the observation that successful operation of a robot does not necessarily imply the maximum quality of its outcome. It is sufficient if the quality is kept at a given (lower) level. Such a suboptimal task execution offers the possibility of some additional secondary optimization. The kinematics of the task is modified according to a secondary objective function. The quality is then treated as a constraint in the minimization of this secondary objective function. This optimization can be based on biomechanical principles. Here the principle: “learn from humans” is adopted, i.e. the kinematics modification is done so as to resemble the behavior of a human worker. The benefits of the approach of the paper are illustrated by two specific examples, namely the handwriting and spray-coating tasks.

This paper proposes an approach for the design of discrete-time decentralized control systems with m-step delay sharing information pattern, employing the modelbased predictive control (MBPC) scheme combined with fuzzy prediction for the interconnections among the subsystems. A state-space model is used at each control station to predict the corresponding subsystem output over a long-range time period. The interaction trajectories are considered to be non-linear functions of the states of the subsystems. For all cases the interconnections and the necessary predictions for them are estimated by an appropriate adaptive fuzzy identifier based on the generation of linguistic IF-THEN rules and the on-line construction of a common fuzzy rule base. Representative computer simulation results are provided and compared for nontrivial example systems.

This paper proposes an approach for the design of discrete-time decentralized control systems covering not only the case of m-step delay sharing information pattern, but also any general nonclassical information pattern where the non-local information is not spread among the subsystems. It employs the model-based predictive control (MBPC) scheme combined with fuzzy prediction for the interconnections among the subsystems. A state space model is used at each control station to predict the corresponding subsystem output over a long-range time period. The interaction trajectories are considered to be non-linear functions of the states of the subsystems. In all cases, the interconnections and the necessary predictions for them are estimated by an appropriate neuro-fuzzy identifier trained on-line using the back-propagation training algorithm. Representative computer simulation results are provided and compared for nontrivial example systems.

In this paper, an overviewing discussion of DSS and AI issues applied to Geographical Information Systems (GIS) is provided. The fundamental concepts and components involved in these systems are included. along with the major problems where they can be profitably used. Four particular real-life applications (case studies) are outlined. These are : (i) the Athens real-time traffic congestion system, (ii) the best route facility, (iii) the route cost comparing system. and (iv) the traffic preemption system. The paper concludes with a discussion on the capabilities of GIS enhanced with DSS and AI functions.

Robots of all types constitute one of the keys to human social and economic development. In particular, the benefits of using autonomous and intelligent mobile robots in medical, assistive, and service applications are numerous, and their positive impact in modern society is steadily increasing. The purpose of the present chapter is to provide an encyclopedic tour to a small set of mobile robots of various types that are actually used in modern industry and society. Specifically, the following robot categories are discussed with relevant photos included: (i) mobile robots and manipulators in the factory and industry, (ii) mobile robots in the society (rescue, guidance, hospital), (iii) mobile robots for home services (cleaning, other services), (iv) assistive mobile robots (autonomous wheelchairs, service mobile manipulators for the impaired), (v) mobile telerobots and web robots, and (vi) other mobile robot applications (entertainment, socialized robots, etc). The above applications illustrate the importance and value of mobile robots for achieving a better quality of human life in a wide variety of directions.

Localization and mapping are two of the basic operations needed for the navigation of a robot, the other operations being path planning and motion planning studied in Chapter 11. Other fundamental capabilities and functions of an integrated mobile robotic system, from the task/mission specification to the motion/task execution, are cognition of the task specification, perception of the environment, and control of the robot motion. This chapter is devoted to the localization and map building operations. Specifically, the objectives of the chapter are (i) to provide the background concepts on stochastic processes, Kalman estimation, and Bayesian estimation employed in WMR localization, (ii) to discuss the sensor imperfections that have to be taken care in their use, (iii) to introduce the relative localization (dead reckoning) concept and present the kinematic analysis of dead reckoning in WMRs, (iv) to study the absolute localization methods (trilateration, triangulation, map matching), (v) to present the application of Kalman filters for mobile robot localization, sensor calibration, and sensor fusion, and (vi) to treat the simultaneous localization and mapping problem via the extended Kalman filter, Bayesian estimation, and particle filter.

Fuzzy logic systems (or, simply, fuzzy systems, FSs) and neural networks are universal approximators, that is, they can approximate any nonlinear function (mapping) with any desired accuracy, and have found wide application in the identification, planning, and model-free control of complex nonlinear systems, such as robotic systems and industrial processes. Fuzzy logic offers a linguistic (approximate) way of drawing conclusions from uncertain data, and neural networks offer the capability of learning and training with or without a teacher (supervisor). The objectives of this chapter are the following: (i) to provide a brief introduction to neural networks and fuzzy systems, (ii) to derive and discuss the general structure of fuzzy and neural robot controllers, (iii) to provide the details of mobile (nonholonomic) fuzzy tracker controller design, (iv) to fuzzy the model-based sliding mode controller and apply it to mobile robots, and (v) to solve the mobile adaptive tracking controller design problem using multilayer perceptrons and radial basis function neural networks.

This chapter deals with the configuration of mobile robots in their workspace, the relations between their geometric parameters, and the constraints imposed in their trajectories. The study of kinematics is a fundamental prerequisite for the study of robot dynamics, stability, and control. The objectives of this chapter are as follows: (i) to present the fundamental analytical concepts required for the study of mobile robot kinematics, (ii) to present the kinematic models of nonholonomic mobile robots (unicycle, differential drive, tricycle, and car-like wheeled mobile robots (WMRs)), and (iii) to present the kinematic models of 3-wheel, 4-wheel, and multiwheel omnidirectional WMRs.

Mobile manipulators (MMs) are robotic systems consisting of articulated arms (manipulators) mounted on holonomic or nonholonomic mobile platforms. They provide the dexterity of the former and the workspace extension of the latter. They are able to reach and work over objects that are initially outside the working space of the articulated arm. Therefore, MMs are appealing for many applications, and today they constitute the main body of service robots. One of the principal and most challenging problems in MMs research is to design accurate controllers for the entire system. The objectives of this chapter are (i) to present the Denavit–Hartenberg method of articulated robots’ kinematic modeling, and provide a practical method for inverse kinematics, (ii) to study the general kinematic and dynamic models of MMs, (iii) to derive the kinematic and dynamic models of a differential drive and a three-wheel omnidirectional MM, with two-link and three-link mounted articulated arms, respectively, (iv) to derive a computed-torque controller for the above differential-drive MM, and a sliding-mode controller for the three-wheel omnidirectional MM, and (v) to discuss some general issues of MM visual-based control, including a particular indicative example of hybrid coordinated (feedback/open-loop) visual control, and an omnidirectional (catadioptric) visual robot control example.

This chapter is concerned with systemic and software architectures that can be used for integrating controllers and high-level functional units to achieve overall intelligent performance of a mobile robot. Software architectures help to deal with the high degree of heterogeneity among the subsystems involved, to face strict operational constraints posed by real-time interactions with the robot’s environment, and to treat the system’s complexity that goes beyond the capabilities of a single designer. The purpose of this chapter is to provide a tour to fundamental concepts and architectures that integrate low-, medium-, and high-level control and planning functions of mobile robotic systems. Specifically, the following issues are considered: (i) hierarchical, multiresolutional, reference model, and behavior-based system control architectures; (ii) basic characteristics of software architectures; (iii) two examples of robot software control architectures; (iv) comparative evaluation of two software architectures; (v) intelligent human–robot interfaces; (vi) two intelligent mobile robot research prototypes (SENARIO, ROMAN); (vii) design for heterogeneity and modularity.

Robot planning is a special case of general planning, which is concerned with the problem of figuring out how the robots should move to get from one place to another, and how to perform a desired task. Actually, the term planning means different things to different scientific communities (artificial intelligence, management, production engineering, etc.). The objectives of this chapter are (i) to present the general conceptual definition of robot path planning, motion planning, and task planning, (ii) to investigate the path planning problem of mobile robots, including the basic operations and classification of methods, (iii) to study in some detail the model-based mobile robot path planning, including configuration space and road map planning methods, (iv) to discuss mobile robot motion planning presenting the vector fields method and the analytical parameterized method, and (v) to outline the basic issues of fixed and mobile robot task planning, including plan representation and generation, and the three phases of task planning, namely, world modeling, task specification, and robot program synthesis.

The use of sensors is of paramount importance for closing the feedback control loops that secure efficient and automated/autonomous operation of mobile robots in real-life applications. Sensing methods provide higher level and intelligence capabilities that go far beyond the “preprogrammed” style of operation. This chapter provides a conceptual introduction to a number of important sensors for mobile robot operation and control. Specifically, the objectives of the chapter are (i) to provide a popular classification of sensors, along with their operational features, (ii) to discuss sonar, laser, and infrared sensors, (iii) to present an outline of robotic vision and its principal functions (including omnidirectional vision), (iv) to list the operation principles of gyroscope, compass, and force/tactile sensors, and (v) to give a brief introduction to the global positioning system.

This chapter presents the fundamental general concepts of mobile robots that can move from one place to another autonomously within a predefined workspace to achieve their desired goals. Specifically, the chapter (i) provides a list of the main historical landmarks of general robotics and mobile robots, (ii) discusses the locomotion issues of ground (wheeled, legged) mobile robots, (iii) investigates the wheel and drive types of mobile robots (nonholonomic, omnidirectional) and, (iv) introduces the concepts of mobile robot degree of mobility, degree of steerability, and maneuverability.

Vision is an important robotic sensor since it can be used for environment measurement without physical contact. Visual robot control or visual servoing is a feedback control methodology that uses one or more vision sensors (cameras) to control the motion of the robot. Specifically, the control inputs for the robot motors are produced by processing image data (typically, extraction of contours, features, corners, and other visual primitives). In robotic manipulators, the purpose of visual control is to control the pose of the robot’s end-effector relative to a target object or a set of target features. In mobile robots, the vision controller’s task is to control the vehicle’s pose with respect to some landmarks. The objectives of this chapter are (i) to present the basic aspects of visual servoing (camera visual transformations, image Jacobian matrix), (ii) to discuss the position-based and image-based visual-control problems, (iii) to apply visual servoing to a number of selected mobile robot control problems, and (iv) to study mobile robot visual servoing using omnidirectional vision.

Mobile robot dynamics is a challenging field on its own, especially due to the variety of the imposed constraints. Delicate stability and control problems that have very often to be faced are due to longitudinal or lateral slip, and to the features of the ground (roughness, etc.). This chapter has the following objectives: (i) to present the general dynamic modeling concepts and techniques of robots, (ii) to study the Newton–Euler and Lagrange dynamic models of differential-drive mobile robots, (iii) to study the dynamics of differential-drive mobile robots with longitudinal and lateral slip, (iv) to derive a dynamic model of car-like wheeled mobile robots, (v) to derive a dynamic model of three-wheel omnidirectional robots, and (vi) to derive a dynamic model of four-wheel mecanum omnidirectional robots.

The objective of this chapter is to present a collection of experimental simulation results drawn from the open literature for most of the methodologies considered in the book. Specifically, the book provides sample results obtained under various artificial and realistic conditions. In most cases, the desired paths to be followed and the trajectories to be tracked are straight lines, curved lines, or circles or appropriate combinations of them. The simulation experiments presented in the chapter cover the following problems treated in the book: (i) Lyapunov-based model reference adaptive and robust control, (ii) pose stabilization and parking control using polar, chained, and Brockett-type integrator models, (iii) deterministic and fuzzy sliding mode control, (iv) vision-based control of mobile robots and mobile manipulators, (v) fuzzy path planning in unknown environments (local, global, and integrated global-local path planning), (vi) fuzzy tracking control of differential drive WMR, and (vii) simultaneous localization and mapping (SLAM) using extended Kalman filters (EKFs) and particle filters (PFs).

This paper introduces and elucidates the concept of model-based predictive control (MBPC) used as a decision-making tool for handling complex integrated production planning problems within a stochastic environment. First, a functional overview of the MBPC methodology is provided, then the framework under which the MBPC approach is used as a decision-making tool is given. Simulation experiments have strongly indicated the flexibility and effectiveness of the proposed approach.

In this paper a review of some fundamental issues of knowledge-based approaches for automated assembly is presented. First, the basic requirements of automated assembly are discussed and the advantages of knowledge-based techniques are highlighted. Then a number of existing models and techniques for knowledge-based assembly are reviewed. Some of them deal with the uncertainty one way or another. It is argued that fuzzy logicand fuzzy reasoning-based techniques are more adequate than others under specific circumstances. A hierarchical assembly expert system which is based on such techniques and is designed and implemented by the authors' group is then described. Finally, some results produced by this system are presented.

This paper deals with the regulation of the thermal characteristics of Gas Metal Arc (GMA) welding. A previously developed model is used for the open loop predictions. At the first level of the hierarchy, a parameterized Generalized Predictive Control (GPC) algorithm is selected among other control techniques, due to its robustness against modeling errors and parameter variations. A coordinator, at the second level of the hierarchy, specifies a set of reliable values for the parameters of GPC, so that stability is assured. A representative set of simulation results is included, along an evaluation of the advantages and limitations of the proposed hierarchical GPC technique.

The response of a previously proposed teleoperator system, which was not based on the traditional notion of telepresence but on an original form of kinesthetic feedback, is tested under modeling errors of up to 50%. The original system is enhanced by replacing the previous controller of each robot with a sliding mode robust one. A two-stage approach is employed: a desired trajectory is first generated and then tracked by the controller. The difficulties incurred for the man/machine cooperation are discussed. This extension of the original teleoperator architecture is tested through simulations and compared with a twin system based on neural controllers, presented elsewhere.

Neural adaptive control (NAC) is naturally a very good candidate for the regulation of the geometrical and thermal characteristics of the welding process which is subject to strong variations and disturbances. Here two different versions of neural adaptive control are tested. The applied control scheme is the Feedback Error Lerning Architecture (FELA). In its flrst version a Multilayered Perceptron (MLP) Neurocontroller is used while its second version is based on a Radial Basis Function (RBF) Neurocontroller. Extensive numerical results are provided along with a discussion of the relative merits and limitations of the above techniques.

This paper presents a Hardware-in-the-loop (HIL) approach on a Field Programmable Gate Array (denoted as FPGA-in-the-loop and abbreviated as FIL) for the optimised sensor selection task of controlling a MAGnetic LEVitation (MAGLEV) system. A recently proposed systematic framework for optimised sensor selection for control and fault tolerance is developed in [1]. The framework was validated using realistic simulations on an electromagnetic suspension system. In this work, the sensor selection framework for control, and practical validation of the controller on FPGA (via HIL concept) applied on an electromagnetic suspension are considered.

This paper presents an Expert Data Rejection (EDR) system, developed to accompany a real-time data acquisition tool for industrial plants. The EDR system is an integrated software tool responsible for the first stage process of multiple raw measurements coming from the plant. It provides different algorithms, which operate in real time, recognizing faulty patterns according to the user requirements. For each specific measurement the user may define a different way of treatment. The system checks the signal values and filters the incoming data. Various adaptive techniques have been adopted, such as digital filtering and variations of the recursive least-squares algorithm, as well as the neural network based algorithms. The system was applied and tested in a subprocess of a refinery plant in Athens. The EDR system removes the measurement noise and provides reliable data for further processing. It also gives information about the existence of faulty instruments monitoring the operation of the industrial process.

This paper presents and investigates a neural network structure which can perform general fuzzy inference. This system consists of a number of parallel neural network units which are called “flexible inference cells” (FICs). Each FIC implements a single-input/single-output (SISO) IF-THEN rule of a fuzzy knowledge base. The assumption of SISO fuzzy rules allows the implementation of any complex fuzzy inference algorithm (for control or other decision making purposes), since any MIMO (multi-input/multi-output) rule can be decomposed into an equivalent set of MISO (multi-input/single-output) rules, and a MISO rule can be decomposed to a set of SISO rules. The paper discusses the assumptions and requirements for the proposed neurofuzzy structure, and classifies the FICs into four categories. Some results derived by simulation using 3125 exemplar patterns produced computationally are provided.

Flexible and dexterous manipulation of modern industrial applications requires the use of robots with extra (more than six) axes of motion (degrees of freedom). Thus the kinematic and control considerations of redundant robots are currently of increasing interest to both theorists and practitioners. For this type of robots, a closed-form solution of the inverse kinematic problem is not possible, and the classical approach is to use the pseudoinverse of the Jacobian matrix together with an extra criterion function. This paper presents an iterative technique for velocity control, which is suitable for redundant robots with a maximum of 11 d.o.f. This technique, which splits the Jacobian inversion problem into two subproblems (wrist position and wrist orientation), provides an exact solution for the arm tip position and an approximate solution for the wrist orientation. The convergence of the procedure is examined and a simulated example is included.

Neural networks or connectionist models are massively parallel interconnections of simple neurons that work as a collective system, can emulate human performance and provide high computation rates. On the other hand, fuzzy systems are capable to model uncertain or ambiguous situations that are so often encountered in real life. One way for implementing fuzzy systems is through utilizations of the expert system architecture. Recently, many attempts have been made to “fuse” fuzzy systems and neural nets in order to achieve better performance in reasoning and decision making processes. The systems that result from such a fusion are called neuro-fuzzy inference systems and possess combined features. The purpose of the present paper is to propose such a neuro-fuzzy system by extending and improving the system of Keller et al. (1992). The present system makes use of Hamacher's fuzzy intersection function and Sugeno's complement function. After a brief outline of the operation of the system its features are established with the aid of four theorems which are fully proved. The capabilities of the system are shown by a set of simulation results derived for the case of trapezoidal fuzzy sets. These results are shown to be better than the ones obtained with the original neuro-fuzzy system of Keller et al.

A very important subject for the consolidation of neural networks is the study of their capabilities. In this paper, the relationships between network size, training set size and generalization capabilities are examined. The phenomenon of overtraining in backpropagation networks is discussed and an extension to an existing algorithm is described. The extended algorithm provides a new energy function and its advantages, such as improved plasticity and performance along with its dynamic properties, are explained. The algorithm is applied to some common problems (XOR, numeric character recognition and function approximation) and simulation results are presented and discussed.

This chapter presents the state-of-art of the bilateral teleoperation field. It starts with a discusion of the early class of techniques, which are based on passivity and scattering theory. The main issue in bilateral telerobotic systems is the communication delay between the operator and the remote site (environment), which (if not treated) can lead the system to instability. The chapter continues by presenting the evolution of modern control techniques for stabilization and compensation of the time delay consequences. These techniques include predictive control, adaptive control, sliding-mode robust control, neural learning control, fuzzy control, and neurofuzzy control. Four case studies are reviewed that show what kind of results can be obtained.

Flexibility and versatility are two basic requirements for industrial robots used in manufacturing areas. In the present paper, a kinematic transformation for redundant robots between Cartesian and joint coordinates is achieved in the trajectory planner. This problem is treated by taking into account both the dynamics of the system expressed by its Lagrangian and the kinematic constraints of the robot. Given the joint trajectories as above, a control law for joint trajectory tracking is generated here using the Model Based Predictive Control (MBPC) approach. The MBPC strategy is based on an explicit robot model to predict the process output over a long-range time period. The postulated control law is constructed such that to fit a desired command acceleration for the servo motors of the robotic system. The actual control law is calculated by minimizing a suitable objective function. A numerical example concerning a robot with one degree of redundancy illustrates the method and shows the excellent performance of the proposed MBPC scheme. The drifting problem that emerges in the resolved motion control of redundant robots is successfully faced.

Proportional plus integral plus derivative (PID) control is a well established technique for industrial control and possesses a high flexibility for use in many applications. However the tuning procedure is in many situations nontrivial, which implies that it is difficult to find the values of the parameters that guarrantee a desired closed-loop performance. The purpose of this paper is to present a PID-tuning technique which is based on a combination of two popular methods; namely the pole placement and the parameter optimization methods. The resulting PID self-tuning controller was applied to several simulated examples. This study showed that the controller has an improved performance over the simple pole placement or the simple parameter optimization self-tuning PID controller.

The problem of optimizing the operation of an autonomous manufacturing/supply chain is considered. The chain consists of an upper level manufacturer and a hierarchy of intermediate suppliers/subsuppliers that provide the required materials. Each subsystem involved in the chain incurs some costs that have to be handled effectively. This paper considers the case of ordering and holding costs. It is assumed that each level in the chain may add some delay in the materials' procurement due to the extra processing taking place or to the time required to supply the corresponding materials. Three possible scenaria are examined. In scenario I, the manufacturer optimizes his own costs and imposes the resulting policy to the (sub) suppliers without any question. In scenario II, all levels of the chain are assumed to cooperate in the sense that their common objective is to minimize the overall operational cost. Finally, in scenario III, each level is optimizing a local cost function in a decentralized way, which may be in conflict with that of the other levels. A partial cooperation is assumed, in the sense that each part minimizes its own costs, plus appropriately weighted deviations of its order profile from a set of calculated reference variables that depend upon the dynamics of the immediate downstream level. A complete numerical example illustrates the above cases.

Five scheduling problems are considered, concerning unit-length independent tasks and uniform machines. New improved optimal algorithms are presented that can solve these problems in at most O( n log n) time, where n is the number of tasks. The existing algorithms solve most of these problems in O( n 3 ) time. Proofs of optimality of the present algorithms are included, and simple representative examples are provided that illustrate the type of results obtained

In this paper, a new adaptively weighted combinatorial dispatching (AWCD) rule is developed which is appropriate for complex scheduling problems with multiple and conflicting criteria. This rule identifies the constraints that are most tight, and adaptively estimates the tightness of each constraint. Previous combinatorial dispatching rules appear as special cases of the AWCD rule. An illustrative small-scale scheduling example is provided, which shows the applicability of the AWCD rule to practical problems. Statistical results obtained using 200 random scheduling problems showed the superiority of the AWCD rule over six other composite or combinatorial rules.

The control of mobile robots is an intense research field, having produced a substantial volume of research literature in the past three decades. Mobile robots present a challenge in both theoretical control design as well as hardware implementation. From a theoretical point of view, mobile robots exert highly non-linear kinematics and dynamics, non-holonomy and complex operational environment. On the other hand, hardware designs strive for scalability, downsizing, computational power and low cost. The main control problems in robot motion control can be classified in three general cases; stabilization to a point, trajectory tracking and path following. Point stabilization refers to the stabilization of the robot to a specific configuration in its state space (pose). For example, if the robot at t = t0 is at p0 = (x0, y0, θ0), then find a suitable control law that steers it to a goal point pg=(xg, yg, θg). Apparently p0 must be an equilibrium point of the closed loop system, exerting asymptotic stability (although practical stability can also be sought for). In the path following (or path tracking) problem, the robot is presented with a reference path, either feasible or infeasible, and has to follow it by issuing the appropriate control commands. A path is defined as a geometric curve in the robot’s application space. The trajectory tracking problem is similar, although there is a notable difference; the trajectory is a path with an associated timing law i.e. the robot has to be on specific points at specific time instances. These three problems present challenges and difficulties exacerbated by the fact that the robot models are highly non-linear and non-holonomic (although robots that lift the non-holonomic rolling constraint do exist and are called omni-directional robots. However the most interesting mobile robots present this constraint). The non-holonomy means that there are constraints in the robot velocities e.g. the non-slipping condition, which forces the robot to move tangentially to its path or equivalently, the robot’s heading is always collinear to its velocity vector (this can readily be attested by every driver who expects his car to move at the direction it is heading and not sideways i.e. slip). For amoremotivating example of holonomy, consider a prototypical and pedagogical kimenatic model for motion analysis and control; the unicycle robot, described by the equations

Scheduling problems deal with the assignment of tasks, over time, to processors, so as to optimize a performance criterion, while satisfying a set of given constraints. In this paper a comprehensive survey of the major results in this area is presented. Specifically, a unified formulation of deterministic scheduling models (problems) is provided on the basis of the three standard data fields, viz. the task set, the processor set, and the optimality criterion set. Some particular practical interpretations of these problems are briefly outlined, and a general discussion on their solution approaches is included. The main body of the paper examines the classes of “parallel processor scheduling” and “resource constrained scheduling” problems. In both classes the following cases are considered: (i) makespan minimization, (ii) maximum lateness minimization, (iii) mean and mean weighted flow time minimization, and (iv) maximum weighted lateness minimization. The paper ends with a discussion of some problems of the sequencing, open-shop, job-shop and flow-shop types, and with some remarks on the potentiality of artificial intelligence and expert systems to solve difficult scheduling problems.