This paper mainly addresses the formation control problem for a group of tail-sitters in transition flight between forward and vertical flight. A robust formation control method is proposed to achieve the aggressive time-varying formation subject to nonlinear dynamics and uncertainties. For each tail-sitter, the proposed control method results in a composite controller that includes a trajectory tracking controller and an attitude controller to achieve the translational and rotational motion control, respectively. It is proven that tracking errors of the proposed global closed-loop system can converge to a given neighborhood around the origin in a finite time. Finally, the simulation studies for multiple tail-sitters to accomplish the time-varying formation in transition flight are presented to show the effectiveness of the proposed control strategy.

This paper addresses an error-driven nonlinear feedback design technique to improve the dynamic performance of fuzzy adaptive dynamic surface control (DSC) for a class of uncertain multiple-input-multiple-output nonlinear systems with prescribed tracking performance. The highlight of the error-driven nonlinear feedback technique is that the feedback gain self-regulates versus different levels of output and virtual tracking errors, this reflects the classical control design criterions commendably: relatively high feedback gains can be implemented to guarantee disturbances and uncertainties attenuation and so on to improve the control performance when small tracking errors are measured, and relatively small feedback gains can be implemented to circumvent the problems of actuator and states saturations when large tracking errors are measured. The complexity problem of the traditional backstepping design is circumvented owe to the peculiarity of DSC method. Caused by the compound error functions of nonlinear feedback dynamics, a nonquadratic Lyapunov function is used to deduce the conditions of closed-loop stability. Fuzzy logic systems and error transformation-based method are used in the online learning of completely unknown dynamics and the prescribed performance tracking, respectively. Comparative results are presented to demonstrate the effectiveness and preponderance of the proposed control scheme with comparison to existing ones.

Ground effect refers to the apparent increase in lift that an aircraft experiences when it flies close to the ground. For helicopters, this effect has been modeled since the 1950's based on the work of Cheeseman and Bennett, perhaps the most common method for predicting hover performance due to ground effect. This model, however, is based on assumptions that do not hold for small-scale rotorcraft because it was developed specifically for conventional helicopters. It is not clear if the Cheeseman-Bennett model can be applied to today's multirotor UAVs. In this paper, we compare the Cheeseman-Bennett model to experimental results for rotor performance due to ground effect in several small-scale multirotor and single-rotor configurations. Experimental findings suggest that some of the conventional thinking surrounding helicopter ground effect cannot be applied directly to rotorcraft using fixed propellers at variable speeds (e.g. multirotors), and that it is necessary to adjust the helicopter models to better reflect the differences in such aircraft. The experimental results for multirotors presented are for multiple propeller configurations, speeds and spacings. Ultimately, this work will facilitate the development of an improved UAV flight controller that can accurately account for ground effect to improve flight stability near surfaces and structures.

This paper proposes a real-time system for pose estimation of an Unmanned Aerial Vehicle (UAV) using parallel image processing of a known marker. The system exploits the capabilities of a high-performance CPU/GPU embedded system in order to provide on-board high-frequency pose estimation, eliminating the need for transmitting the video stream off-board, and enabling autonomous takeoff and landing. The system is evaluated extensively with lab and field tests on board a small quadrotor. The results show that the proposed system is able to provide precise pose estimation with a framerate of at least 30 fps and an image resolution of 640×480 pixels. The use of the GPU for image filtering and marker detection provides an upper bound on the required computation time regardless of the complexity of the image thereby allowing for robust marker detection even in cluttered environments.

A semi-autonomous mobile self-leveling landing platform designed to launch, recover and re-launch VTOL UAVs without the need for human intervention is described. The landing platform is rugged, lightweight and inexpensive, making it ideal for civilian applications that require a base station from which a rotorcraft UAV can be launched and/or recovered on terrain that is normally unsuitable for UAV take-off and landing. This landing platform is capable of autonomously self-leveling on rough terrain and inclines up to 25°, and can operate in isolated remote locations for extended periods of time using large onboard lithium batteries and wireless communication. The unique design aspects of this landing platform are that it is mobile, self-leveling, and man-portable. A fully-operational prototype has been designed, constructed and evaluated. Design details and experimental results are presented to demonstrate the landing platform's functionality, and that all primary design requirements have been met.

A limitation of small-scale, autonomous, vertical take-off and landing (VTOL) vehicles is their relatively short flight time. This hinders their broad applicability for many commercial applications. In previous work, we present the design and implementation of ISLANDS, an autonomous self-leveling landing platform for VTOL vehicles, along with an initial approach to their placement in the field of work. In this paper, we present several new approaches for improved station placement in a field of work of arbitrary shape and size, given sensor characteristics and the nature of the application. We present these algorithms in the context of a generic survey application, but they generalize to many other applications such as search and rescue, traffic monitoring, and environment monitoring.

The main goal of this paper is to describe a new methodology to evaluate the I/O responsiveness of microprocessors. This addresses the need for benchmarks for real-time systems that measure critical properties for system design that are not currently handled by traditional performance benchmarks. The benchmark developed under this methodology is tested on three microprocessor architectures: ARM, PIC and XMOS. The results of the benchmarks show that the response latency of systems can vary significantly between architectures and under different system loads.

In engineering design optimization, derivatives are computationally expensive and/or unreliable, therefore evolutionary optimization techniques are preferred, such as Particle Swarm Optimization. Particle Swarm Optimization is still young in development and is being expanded to many different areas such as equality constrained optimization problems and multi-objective optimization. This paper proposes a new algorithm to determine a Pareto Front with a single two case equation. The new algorithm combines Domination Theory from Multi-Objective Optimization with Swarm Theory to determine a well represented Pareto Front by performing a single optimization simulation.

In this paper, we present a strategy for organizing swarms of unmanned vehicles into a formation by utilizing artificial potential fields that were generated from normal and sigmoid functions. These functions construct the surface on which swarm members travel, controlling the overall swarm geometry and the individual member spacing. Nonlinear limiting functions are defined to provide tighter swarm control by modifying and adjusting a set of control variables that force the swarm to behave according to set constraints, formation, and member spacing. The artificial potential functions and limiting functions are combined to control swarm formation, orientation, and swarm movement as a whole. Parameters are chosen based on desired formation and user-defined constraints. This approach is computationally efficient and scales well to different swarm sizes, to heterogeneous systems, and to both centralized and decentralized swarm models. Simulation results are presented for a swarm of 10 and 40 robots that follow circle, ellipse, and wedge formations. Experimental results are included to demonstrate the applicability of the approach on a swarm of four custom-built unmanned ground vehicles (UGVs).

This work addresses the problem of coordinating a swarm of unmanned ground vehicles with an unmanned aerial vehicle (UAV). The UAV is utilized as a leader robot that is intelligently followed by a coordinated group of unmanned ground vehicles (UGVs). The UAV is a completely autonomous agent that is controlled using Sugueno fuzzy logic. The UGVs are organized into formation utilizing artificial potential fields generated from normal and sigmoid functions. These functions are built around the location of the UAV and ultimately construct the surface that swarm members travel on, which inherently controls the overall swarm geometry and the individual member spacing. Nonlinear limiting functions are defined to provide tighter swarm control by modifying and adjusting a set of control variables forcing the swarm to behave according to set constraints, formation and member spacing. The swarm function and limiting functions are combined to control swarm formation, orientation, and swarm movement as a whole. Parameters are chosen based on desired formation as well as user defined constraints. Simulations demonstrate the precision of the approach with up to forty UGVs. Experimental results are presented using a fully autonomous swarm of three UGVs and a single UAV helicopter for coordination.

This paper presents theoretical details of a model-based sensor fault detection and isolation system (SFDIS) applied to unmanned ground vehicles (UGVs). Structural analysis is applied to the nonlinear model of the vehicle for residual generation. Two different solutions have been proposed for developing the residual evaluation module. The vehicle sensor suite includes a global positioning system (GPS) antenna, an inertial measurement unit (IMU), and two incremental optical encoders.

This paper presents implementation details of a model-based sensor fault detection and isolation system (SFDIS) applied to unmanned ground vehicles (UGVs). Structural analysis, applied to the nonlinear model of the UGV, is followed to build the residual generation module, followed by a residual evaluation module capable of detecting single and multiple sensor faults, as detailed in part I (Monteriu et al., 2007). The overall proposed sensor fault detection and isolation system has been tested in real-time on the ATRV-Jr mobile robot when following different trajectories in an outdoors environment. The robot sensor suite includes a global positioning system (GPS) antenna, an inertial measurement unit (IMU), and two incremental optical encoders

Small unmanned vertical take off and landing vehicles are used to provide the eye-in-the-sky alternative to monitoring and regulating traffic dynamically. Spatial-temporal visual data are collected in real time and they are used to generate traffic-related statistical profiles, serving as inputs to traffic simulation models. Generated profiles, which are continuously updated, are used to calibrate traffic model parameters, to obtain more accurate and reliable simulation models, and for model modifications. This method overcomes limitations of existing traffic simulation models, which suffer from outdated data, poorly calibrated parameters because of outdated data, questionable accuracy and poor predictions of traffic patterns.

This paper presents a practical and simple approach to the design of autonomous flight control of miniature helicopters. It considers a standard easily tunable PID control as the starting step of the design. Then, robust loop-shaping techniques are applied to derive a controller with optimal properties with respect to robustness, noise sensitivity, and bandwidth. Comprehensive analysis and simulation experiments show the validity and practicality of the proposed control design and analysis procedures.

This paper presents a low cost, high powered onboard processing system for miniature unmanned vehicles (UVs), especially unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). Such vehicles have shown great potential due to their compact size, high maneuverability and high payload-to-size ratio. The proposed on-board processing system differs from other existing designs by utilizing developed commercial off the shelf (COTS) hardware and a full, nongraphical, operating system (OS). The hardware allows for quick development and deployment of the on-board processing system as well as easy integration with new hardware. The full OS allows for simplified software development and testing as well as the use of commercial and open source software. The physical configuration of the system, necessary drivers, and field tests on both aerial and ground vehicles are presented

Localization is a key function in Wireless Sensor Networks (WSNs). Many applications and internal mechanisms require nodes to know their location. This work proposes a new sequential estimation algorithm for distributed cooperative localization, whose simplicity makes it amenable to self-localization in Wireless Sensor Networks (WSNs), characterized by their restricted resources in energy and computation. The algorithm is inspired in sequential Monte-Carlo estimation techniques, viz. particle filters, that excel in robustness and simplicity for estimation applications. However, particle filters require significant amounts of memory and computational power for managing large numbers of particles. The presented technique reduces the number of particles, while retaining the convergence, accuracy and simplicity properties, as demonstrated in simulation experiments.

Hybrid timed Petri nets (HTPNs) are derived to study random topology and complexity multioperational production systems where parts of one type follow the same route to produce a final product. Each production system is first decomposed into a fundamental multiproductive machine, multiassembly and multidisassembly modules, followed by derivation of their corresponding HTPN models. The overall system HTPN model is obtained via individual module synthesis, satisfying system constraints. Individual module and overall HTPN models nodes (places and transitions) are calculated. Individual module and overall system model invariants are derived mathematically. Performance and possible tradeoffs due to varying operational constraints (buffer capacity, work in process, machine utilization, backlog, etc.) are investigated through extensive simulations. Results show the applicability of the proposed methodology and justify its modeling power and generality.

As mobile robot teams become more complex, it is necessary to develop a control architecture to manage the resources present in the team. The Distributed Field Robot Architecture (DFRA) is a distributed, object-oriented implementation of the SFX hybrid robot architecture that allows for dynamic discovery and acquisition of robot resources and the seamless integration of humans and artificial agents in the robot team. This paper introduces the DFRA and details its application to a high-fidelity demining scenario using a heterogeneous team of ground and aerial robots.

Alternative designs of a simple, low cost, effective vision system suitable for small, portable, unmanned vertical take off and landing (VTOL) vehicle based surveillance and landmark recognition are presented. Designs depend mainly on two critical constraints: aerial vehicle payload limitations and power supply limitations; both constraints affect on-board vision system configuration, processing power and flexibility. Hardware and software components are described for both designs. Advantages and disadvantages of both alternatives are compared and an implementation of the chosen system is presented along with experimental results.

A simplified analysis is presented for small scale unmanned helicopter controller design suitable for non-aggressive flight scenarios. A Matlab based detailed approach has been followed to analyze system mathematical characteristics, and provide the design process tool for PID/PD and fuzzy logic controllers for the yaw, pitch, roll (Euler angles) and the height variables for hover and slow flight. Both controllers have performed well; have demonstrated promising results being at the same time simple and mathematically sound.

A two module fuzzy logic controller that also includes a separate error calculating box is derived for autonomous navigation and control of small manned- unmanned aerial vehicles demonstrating ability to fly through specified waypoints in a 3-D environment repeatedly, perform trajectory tracking, and, duplicate/follow another vehicle's trajectory. A MATLAB standard configuration environment and the Aerosim Aeronautical Simulation Block Set are utilized for simulation studies, presented through a visualization interface; results illustrate controller performance and potential.

The paper presents a machine vision system for aerial surveillance that can interpret and process data acquired by a UAV on-board infrared camera. System components include noise reduction, feature extraction, classification and decision-making. Decision-making is performed in terms of an alarm signal. The system has been configured for automatic fire detection applications where the alarm is set off in case of fire identification. Real time tests have been performed and the system has been tested producing sets of real images. Finally, a genetic algorithm was used to automatically define some of the system's parameters.

Parametric technology based design specifications for a new unmanned Vertical Take-Off and Landing (VTOL) vehicle are presented. The design phase is followed by performance capabilities evaluation and crash/drop tests that determine VTOL strength and frame deformations. Drop tests performed have followed standard regulations used for airworthiness certification of such vehicles. Initial drop tests have dictated a frame re-design phase that has met set VTOL specifications. The Pro/Engineer specialized Computer Aided Design (CAD) software has been used throughout all phases and tests.

This work presents a modular hybrid timed Petri net (MHTPN) methodology used to study multi-operational production systems in which parts are used for the production of different product types. This research generalizes work presented in Tsinarakis, GJ et al., (2003) that studied production systems where all parts of the same type were used for the production of a type of final products following a common route. 3 fundamental system modules are derived; their MHTPN models are defined, followed by modules synthesis to obtain the overall system Petri net. For any topology, system's nodes and invariants are calculated. Applicability of the method is illustrated through an analytical example.

This paper presents a method that integrates MATLAB into a distributed behavioral robotics architecture. The architecture is written in Java and uses the Jini platform for distributed object registration, lookup and remote method invocation. The method described here can be used to integrate MATLAB into any Java-based behavioral architecture. The form of the integration allows a running MATLAB workspace to be accessed as a distributed object within the larger Java/Jini-based architecture. This is beneficial because MATLAB scripts and functions may be called in interpreted form and can make full use of MATLAB tool boxes and have access to the MATLAB workspace environment. This is not possible when MATLAB scripts are compiled into stand-alone C++, Java or p-code. The use of the architecture is demonstrated on an iRobot ATRV-JR robot and remote computer workstation. Experiments have been conducted to quantify GPS and odometry errors in outdoor environments using automated methods supported by the distributed architecture.

A supervisory controller is derived for scheduling (single/multiple-part-type, reentrant) production networks. The supervisory controller is used to tune a set of lower level distributed fuzzy control modules that reduce work-in-process (WIP) and synchronize the production system's operation. The overall production-control system is viewed as a two-level surplus-based system with the overall control objective to keep the WIP and cycle time as low as possible, while maintaining quality of service by keeping backlog to acceptable levels. The production rate in each production stage is controlled to satisfy demand, avoid overloading, and eliminate machine starvation or blocking. The system's improvement is demonstrated using a set of performance measures. Extensive simulation results show that the supervisory controller, when compared with the single-level distributed fuzzy controllers reduces WIP and cycle time while keeping backlog to acceptable levels.

Ordinary t-timed Petri nets are used for modeling, analysis and synthesis of random topology production systems and networks. Each production system is first decomposed into production line (transfer chain), assembly, disassembly and parallel machines modules and then their corresponding modular Petri net models are derived. The overall system PN model is obtained via synthesis of the generic modules satisfying system constraints. P- and T-invariants are calculated and given a random topology production system, the total number of the system PN model nodes (places, transitions) is calculated from the corresponding generic PN modules. Results show the applicability of the proposed methodology.

Modular hybrid timed Petri Nets are used to model and analyze behavior of random topology multi-operational production systems. Each such system is decomposed into 3 fundamental modules, followed by derivation of their corresponding modular HPN models. The overall system model is obtained via synthesis of the individual modules, satisfying system constraints (routes, production rates, machine expected up- down- or idle- time and priorities). For any topology multi-operational production system, overall HTPN model nodes are calculated, followed by derivation of model invariants. Results show applicability of the proposed methodology and justify its modeling power and generality.

An evolutionary algorithm based framework, a combination of modified breeder genetic algorithms incorporating characteristics of classic genetic algorithms, is utilized to design an offline/online path planner for unmanned aerial vehicles (UAVs) autonomous navigation. The path planner calculates a curved path line with desired characteristics in a three-dimensional (3-D) rough terrain environment, represented using B-spline curves, with the coordinates of its control points being the evolutionary algorithm artificial chromosome genes. Given a 3-D rough environment and assuming flight envelope restrictions, two problems are solved: i) UAV navigation using an offline planner in a known environment, and, ii) UAV navigation using an online planner in a completely unknown environment. The offline planner produces a single B-Spline curve that connects the starting and target points with a predefined initial direction. The online planner, based on the offline one, is given on-board radar readings which gradually produces a smooth 3-D trajectory aiming at reaching a predetermined target in an unknown environment; the produced trajectory consists of smaller B-spline curves smoothly connected with each other. Both planners have been tested under different scenarios, and they have been proven effective in guiding an UAV to its final destination, providing near-optimal curved paths quickly and efficiently.

A two-layer fuzzy logic controller was designed for 2D autonomous navigation of a skid steering vehicle in an obstacle filled environment. The first layer of the fuzzy controller provides a model for multiple sonar sensor input fusion and it is composed of four individual controllers, each calculating a collision possibility in front, back, left and right directions of movement. The second layer consists of the main controller that performs real-time collision avoidance while calculating the updated course to be followed by the vehicle. The two layer controller applicability and implementation is demonstrated through experimental results and case studies performed on a real mobile robot.

A framework for multi-user distributed virtual environments (DVEs) has been proposed. The proposed framework, incorporating two models (the functional model and the interconnection model), attempts to represent the common functionality, communication issues and requirements found in multi-user DVEs. The functional model concentrates on the DVE's functionality, while the interconnection model concentrates on how the components are interconnected to realize the required functionality. The models have been specified using the Unified Modeling Language (UML). An experimental case study demonstrates the applicability and generality of the proposed approach.

An electrostatic potential field (EPF) path planner is combined with a two-layered fuzzy logic inference engine and implemented for real-time mobile robot navigation in a 2-D dynamic environment. The environment is first mapped into a resistor network; an electrostatic potential field is then created through current injection into the network. The path of maximum current through the network corresponds to the approximately optimum path in the environment. The first layer of the fuzzy logic inference engine performs sensor fusion from sensor readings into a fuzzy variable, collision, providing information about possible collisions in four directions, front, back, left and right. The second layer guarantees collision avoidance with dynamic obstacles while following the trajectory generated by the electrostatic potential field. The proposed approach is experimentally tested using the Nomad 200 mobile robot.

Discusses the design and modeling fundamentals of a multiple degree of freedom reconfigurable robotic gripper system (RGS), designed to reliably automate the process of limp material handling without causing distortion, deformation and/or folding. The reconfigurable gripper design draws upon the authors' (2000) previously reported work on the flat surfaced, fixed-dimensions gripper system. The design of the reconfigurable gripper system consists of four arms in a cross-bar configuration, with a flat surfaced, fixed dimensions, suction based gripper unit mounted on each of the arms. In the paper, the kinematic and dynamic performances of the reconfigurable robotic gripper system are analyzed and then validated using 1-DEAS software.

Discusses the design and modeling fundamentals of a multi-degree-of-freedom reconfigurable robotic gripper system (RGS), designed to automate the process of limp material handling, reliably and without distortion, deformation, and/or folding. The reconfigurable gripper design draws upon a previously reported flat surfaced, fixed-dimensions gripper system (Kolluru et al., 1998). The design consists of four arms in a cross-bar configuration, with a flat surfaced, fixed dimensions, suction-based gripper unit mounted on each of the arms. The kinematic and dynamic performance of the reconfigurable RGS is analyzed theoretically and then validated using Integrated Design Engineering Analysis Software (I-DEAS) simulation software.

Proposes a solution to the two-dimensional (2-D) collision fee path planning problem for an autonomous mobile robot utilizing an electrostatic potential field (EPF) developed through a resistor network, derived to represent the environment. No assumptions are made about the amount of information contained in the a priori environment map (it may be completely empty) or the shape of the obstacles. The well-formulated and well-known laws of electrostatic fields are used to prove that the proposed approach generates an approximately optimal path (based on cell resolution) in a real-time frame. It is also proven through the classical laws of electrostatics that the derived potential function is a global navigation function (as defined by Rimon and Koditschek, 1992), that the field is free of all local minima and that all paths necessarily lead to the goal position. The complexity of the EPF generated path is shown to be O(mn/sub M/), where m is the total number of polygons in the environment and n/sub M/ is the maximum number of sides of a polygonal object. The method is tested both by simulation and experimentally on a Nomad200 mobile robot platform equipped with a ring of sixteen sonar sensors.

Specification of networking requirements for networked virtual reality applications and a way of propagating the user/application level requirements down to the communication level are open research issues. As a contribution to ongoing research, we propose a model, named interconnection model, that represents a network-oriented view of a distributed virtual environment. Within the model, bandwidth requirements are addressed in more detail for shared virtual objects that change as a result of user interactions. A prototype multi-user distributed virtual environment for teleoperation of a mobile robot has been used as a case study.

Describes a visual monitoring system that performs scene segmentation based on color and texture information. Color information is combined with texture, and corresponding segmentation algorithms are developed to detect and measure changes (loss/gain) in a given scene or environment over a period of time. The xyY color space is used to represent the color information. The two chromaticity coordinates (x, y) are combined into one, thus providing the chrominance (spectral) part of the image, while Y describes the luminance (intensity) information. The proposed color/texture segmentation system processes luminance and chrominance separately. Luminance is processed in three stages: filtering, smoothing and boundary detection. Chrominance is processed in two stages: histogram multi-thresholding and region growing. Two or more images may be combined at the end in order to detect scene changes, using logical pixel operators. As a case study, the methodology is used to determine wetland loss/gain. For comparison purposes, results in both the xyY and HIS (hue, intensity, saturation) color spaces are presented.

Ultrasonic range sensors are used to obtain the information required for collision-free navigation of a mobile robot in a semi-structured or unstructured environment. A set of range readings from a ring of sonars are correlated to acquire a 2D map of the robot's environment. The map is continuously enhanced via novel matching and update algorithms as new data are collected while the robot is in motion. The algorithm utilizes confidence measures that are directly obtained from the sonar's resolution or accuracy. There are no additional modeling assumptions and the approach is robust. The algorithms are experimentally tested on the Nomad 200 mobile robot and Nomad's Cognos Software Development Package.

This paper presents an analytical model of a flat surfaced robotic gripper designed to automate the process of reliable, rapid and distortion-free limp material handling. The designed gripper prototype is integrated with an industrial robot manipulator. The gripper geometry and its grasp stability are justified. Performance of the overall system is experimentally tested, based on a set of industry dictated operational constraints. It is found that the gripper system has high reliability, grasp stability, and that it is capable of rapid rates of manipulation.

Focuses on the hierarchical, sensor based control structure for the robotics gripper system designed for handling deformable, materials. The hardware and software implementation of the designed system are used to prototype and test the system. From the presented results, it can be concluded that the robotic gripper system integrated with the implemented control architecture, performs at its fullest potential, with highest performance when using both visual and non-visual proximity sensors to assist the process of object manipulation. While the reliability is tremendously high when using vision and proximity sensors for manipulation, the number of panels manipulated per minute decreases significantly, as the speed of the belt increases. It is concluded that if the speed of the belt were to be kept below 50% of its maximum, using both vision and proximity sensors (robot speed of 100%) results in a highly reliable manipulation of about 13-15 panels of fabric per minute, thus exceeding the system requirements of 8-10 panels per minute.

The objective of this paper is to present the design, analytical modeling and performance evaluation of a flat surfaced robotic gripper designed to automate the process of reliable, rapid and distortion-free limp material handling. The geometric model of the gripper and its grasp stability are analyzed leading to justification of the overall system design. The paper reviews the existing state-of-the-art technology in limp material handling, and presents the design and modeling of the robotic gripper system. Finally, the experimental results are presented.

In this paper, hierarchical time-extended Petri nets (H-EPNs), an extended Petri net based modeling and analysis tool, are used to derive the coordination level model of hierarchically decomposable systems, viewed from a three-level hierarchical structure of organization, coordination and execution of tasks. A two-layer (vertical) coordination level framework, consisting of the dispatcher/analyzer and the H-EPN controller is presented. A detailed two sub-level (horizontal) H-EPN controller model is derived to model system operations (including system soft failures). Error classification based on the interaction between the various system coordinators is derived from the H-EPN model. The H-EPN approach preserves multi-resolutional system details as well as effective communication flows between the various subsystems. A simple example illustrates the proposed approach. The important H-EPN properties of boundedness, safeness and reversibility are verified.

A new class of Petri nets (PNs), called parameterized Petri Nets (PPNs), is proposed and applied for planning and coordination of intelligent systems. The PPN approach uses a hierarchical organization to deal with the complexity characteristic of net representations, through parameterization of transitions and the use of generalized or parameterized places in higher level nets. A unique useful characteristic of PPNs is that they can be used to generate a high level, abstract plan, even in the absence of complete information, which may be mapped into a specific plan at a lower level of the hierarchy. The PPN system model structural properties are verified based on the corresponding PN model.>

This paper models, analyzes, accommodates and simulates potential failures which may occur in a materials handling system with extended Petri nets (EPNs). EPNs are shown to be able to cope with various important failure situations that occur in a manufacturing system through the definition of six different types of places. Detailed EPN based subnets are provided to study and accommodate occurred failures. The approach described proposes an efficient methodology to generate an overall EPN design to model and analyze potential system failures at different levels of detail. It has been observed that the study of boundedness properties rather than the safeness properties of a manufacturing system with resource constraints leads to a better defined EPN system model. The subnet structural properties and the overall system properties are preserved. The Stochastic Petri Net Package (SPNP) is used for simulation studies.>

The failure of well-developed techniques for grey-level scalar images when directly applied to remove impulse noise with median filters in color vector images is illustrated. Pertinent criteria to remove noise in color images with median filters and reconstruct the perceptually optimum output of the median filters are derived and justified. The experiments demonstrate noise suppression and minimization of color distortion.>

A probabilistic approach is followed for the modeling of intelligent robotic systems composed of three interactive levels of organization, coordination, and execution of tasks. Probability and entropy functions are used as analytic measures to interpret mathematically the system functions. The modeling constraint obeys the mathematically proven principle of increasing precision with decreasing intelligence. This probabilistic approach has been tested by simulation in connection with overcoming emergency situations in a pressurized water reactor nuclear power plant.>

A general purpose organization level model for robotic assemblies and intelligent robotic systems is proposed. The methodology and algorithm described consider both well-defined and fuzzy/imprecise environments. The user input commands to the system organizer, an IKBS, are linguistic in general and the primitive events-tasks from the task domain are in general interpreted via fuzzy sets. The constraint of task precedence and the concepts of criticality of tasks-events and valid repetitive orderings are introduced to facilitate and optimize the formulation of every complete plan capable of executing a user-requested job. An example demonstrates the applicability of the proposed algorithm to robotic assemblies.>

A class of Petri nets, called extended Petri nets, with multiple types of places, multiple classes of tokens and multiple arcs is proposed. It is utilized for the hierarchical modeling of flexible manufacturing systems, ensuring a priori that the extended Petri net system model obtained is live, bounded, consistent and error free. The proposed method views the operation of the flexible manufacturing system as a process that is decomposed into operations with specified precedence relations. For each operation the required resources are identified, and on the basis of these requirements the overall system is decomposed into a set of finite subsystems. The operation of each subsystem is modeled as an event graph representing a single resource activity cycle. The extended Petri net system model is synthesized from these component nets using certain synthesis rules. A software package has been developed to simulate the execution of the model obtained.>

The first step in the creation of a robotic manipulator controller performance database has been completed. Experimental evaluation of the trajectory-tracking performance of a PUM-600 under computed-torque control has provided valuable engineering insight into the role of dynamics compensation for manipulators with high torque amplification drive systems. Experimental results validate the assumptions that Coriolis and centrifugal forces are negligible and actuator dynamics play a central role in manipulator dynamics. Without complete modeling of the drive-system dynamics, computed-torque trajectory-tracking accuracy was inadequate for gross motion control. It is concluded that more accurate representation of the real robot system dynamics is required for realistic simulation of modern control algorithms and improved real-time performance.>

Generalized N-dimensional information theory is utilized to describe quantitatively the flow of knowledge (information) within intelligent robotic systems modeled within the constraints imposed by the theory of intelligent controls. A generalized partition law of information rates is derived to describe analytically the laws of information which govern the performance of the three interactive levels of such systems as well as within each of the higher two levels, namely the organization and coordination levels. The law considers the possible trade-offs between the noise, the internal control procedures, the feedback mechanisms, possible coordination, and memory exchange procedures generated during the operation of intelligent robotics systems.>

Evaluation of the effects of dynamics on real-time closed loop robotic manipulator joint control requires accurate, computationally efficient dynamics algorithms. Through a study of open loop joint torques the dominant and negligible torque components for each link of a PUMA-600 have been identified so that prudent model reduction could be accomplished. An acceleration related matrix model which requires only 94 multiplications and 59 additions while achieving accuracy to within ten percent of the complete model has been developed and justified. Reduction of the Newton-Euler PUMA- 600 dynamics to 304 multiplications and 212 additions has also been achieved without any loss of accuracy. The developed algorithms support our real-time study of the effects of dynamics on robot control.

The real-time and simulation performance of the computed-torque joint control technique employing four configurations of dynamic models in controlling a six degree of freedom robotic manipulator has been thoroughly evaluated over a set of twelve different operational situations. Simulation comparisons have been conducted to predict the repercussions of neglected dynamics on computed-torque performance. Real-time tests have been conducted using a PUMA-600 and modified controller. Real-time results do not adhere to the simulation predictions. The algorithm with the simplest dynamics performs best in real-time.

A mathematical formulation of the organization level of an intelligent machine, modeled as a Knowledge Based (Expert) System is derived and analyzed. Characteristic functions of the organization level of such a machine are: machine reasoning, machine planning, decision making, feedback (learning) and memory exchange. Probabilistic models are used to express analytically their individual functions. Information Theory measures and concepts are utilized to describe quantitatively the flow of knowledge in that level as the machine operates. An example illustrates this approach.

Design modifications on the controller of the PUMA- 600 arm are required to apply modern control techniques in real-time. These modifications are performed on the computer-level and the joint control level of the PUMA controller. Real-time closed loop control of the arm is thus made possible. The paper compares the performance of various control algorithms in real-time and by simulation.