This paper presents an evolutionary algorithm (EA) strategy for the optimization of generic work-in-process (WIP) scheduling fuzzy controllers. The EA strategy is used to tune a set of distributed fuzzy control modules that are responsible for WIP scheduling. The objective is to control the production rate in each processing stage in a way that satisfies the demand for final products, while reducing WIP within the production system. The EA identifies the parameters for which a set of fuzzy controllers performs optimal with respect to WIP and backlog minimization. The proposed EA strategy is compared to heuristically tuned distributed fuzzy controllers. Extensive simulation testing shows that the EA strategy significantly improves the system's performance

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.

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.

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.

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.

Flexibility is recognized as an important feature in manufacturing. This paper suggests a knowledge-based methodology for the measurement of manufacturing flexibility. We claim that flexibility is an inherently vague notion and an essential requirement in its measurement is the involvement of human perception and belief. Nine different flexibility types are measured, while the overall flexibility is given as the combined effect of these types. Knowledge is represented via IF (fuzzy antecedents) THEN (fuzzy consequent) rules, which are used to model the functional dependencies between operational characteristics, such as setup time and cost, versatility, part variety, transfer speed, etc. The proposed scheme is illustrated through an example.