Kendall E. Nygard

Complexity in UAV cooperative control

This paper addresses complexity and coupling issues in cooperative decision and control of distributed autonomous unmanned aerial vehicle (UAV) teams. In particular, the recent results obtained by the inhouse research team are presented. Hierarchical decomposition is implemented where team vehicles are allocated to sub-teams using the set partition theory. Results are presented for single assignment and multiple assignments using the network flow and auction algorithms. Simulation results are presented for wide area search munitions where complexity and coupling are incrementally addressed in the decision system, yielding a radically improved team performance.

GIDEON: a genetic algorithm system for vehicle routing with time windows

Addresses the vehicle routing problem with time windows (VRPTW). The VRPTW involves routing a fleet of vehicles, of limited capacity and travel time, from a central depot to a set of geographically dispersed customers with known demands within specified time windows. The authors describe GIDEON, a genetic algorithm system to heuristically solve the VRPTW. GIDEON consists of two distinct modules: a global clustering module that assigns customers to vehicles by a process called genetic sectoring (GENSECT) and a local route optimization module (SWITCH-OPT). On a standard set of 56 VRPTW problems obtained from the literature, GIDEON did better than the alternate methods on 41 of them, with an average reduction of 3.9% in fleet size and 4.4% in distance traveled for the 56 problems. GIDEON took an average of 127 CPU seconds to solve a problem on the Solbourne 5/802 computer.<<ETX>>

Dynamic network flow optimization models for air vehicle resource allocation

A weapon system consisting of a swarm of air vehicles whose mission is to search for, classify, attack, and perform battle damage assessment, is considered. It is assumed that the target field information is communicated to all the elements of the swarm as it becomes available. A network flow optimization problem is posed whose readily obtained solution yields the optimum resource allocation among the air vehicles in the swarm. Hence, the periodic reapplication of the centralized optimization algorithm yields the benefit of cooperative feedback control.

Agent-Oriented Designs for a Self Healing Smart Grid

Electrical grids are highly complex and dynamic systems that can be unreliable, insecure, and inefficient in serving end consumers. The promise of Smart Grids lies in the architecting and developing of intelligent distributed and networked systems for automated monitoring and controlling of the grid to improve performance. We have designed an agent-oriented architecture for a simulation which can help in understanding Smart Grid issues and in identifying ways to improve the electrical grid. We focus primarily on the self-healing problem, which concerns methodologies for activating control solutions to take preventative actions or to handle problems after they occur. We present software design issues that must be considered in producing a system that is flexible, adaptable and scalable. Agent-based systems provide a paradigm for conceptualizing, designing, and implementing software systems. Agents are sophisticated computer programs that can act autonomously and communicate with each other across open and distributed environments. We present design issues that are appropriate in developing a Multi-agent System (MAS) for the grid. Our MAS is implemented in the Java Agent Development Framework (JADE). Our Smart Grid Simulation uses many types of agents to acquire and monitor data, support decision making, and represent devices, controls, alternative power sources, the environment, management functions, and user interfaces.

School bus routing using genetic algorithms

The school bus routing problem involves transporting students from predefined locations to the school using a fleet of school buses with varying capacity. The objective is to minimize the fleet size in addition to minimizing the distance traveled by the buses and the travel time of the students. As the school bus routing problem belongs to the NP-complete class of problems, search strategies based on heuristic methods are most promising for problems in this class. GENROUTER is a system that uses genetic algorithms, an adaptive heuristic search strategy, for routing school buses. The GENROUTER system was used to route school buses for two school districts. The routes obtained by GENROUTER system were superior to those obtained by the CHOOSE school bus routing system and the current routes in use by the two school districts.

Implementation techniques for the vehicle routing problem

Abstract Six methods for implementing the widely used Clarke-Wright algorithm for the vehicle routing problem (VRP) are presented and compared. Fifty-five large test problems are used to compare the methods. The methods involve alternative ways to access adjacency information in both low and high density problems. The results clearly establish methods of choice for VRP problems with given characteristics.

Improving coverage performance in sensor networks by using mobile sensors

Sensor networks hold the promise of facilitating large-scale, real-time data processing in complex environments. Most existing researches on sensor networks consider networks where all sensors are static nodes. We propose to improve sensor network performance by deploying a small number of mobile sensors in addition to a large number of static sensors. In this paper, we present a distributed solution that utilizes a small number of mobile sensors to improve coverage performance in sensor networks. The distributed solution includes distributed schemes for: 1) determining the boundary of a coverage hole; 2) determining the number and locations of mobile sensors for covering a hole; and 3) calling mobile sensors. We design simulation experiments to evaluate the performance of three algorithms that determine the number and locations of mobile sensors for covering a hole. Our experiments show that the integer linear programming algorithm achieves the best results with the cost of high computation requirement, while the other two heuristic algorithms achieve good sub-optimal results with small computation requirement.

Synchronized multi-point attack by autonomous reactive vehicles with simple local communication

We present a model consisting of a swarm of unmanned, autonomous flying munitions to conduct a synchronized multi-point attack on a target. The unpiloted air vehicles (UAV) lack global communication or extensive battlefield intelligence, instead, relying on passive short-range sensors and simple, inter-agent communication. The multi-point synchronized attack is successfully demonstrated in a simulated battlefield environment. The simulation results indicate that the reactive, synchronized, multi-point attack is effective, robust and scalable. It is especially well suited for numerous, small, inexpensive, and expendable UAV.

REPARE: Regenerator Placement and Routing Establishment in Translucent Networks

Most research works in routing and design of optical networks assume that the optical medium can carry data signals without any bit error. However, physical impairments of the optical signal introduced by optical fibers and components, e.g., power loss, noise, and dispersions, impose fundamental constraints in WDM networks, and must be taken into consideration in the routing and design problems of WDM networks. Only through 3R (optical-electrical-optical) regeneration (reamplification, reshaping, retiming) with OEO conversion can a lightpath be recovered from those impairments. Because 3R regenerators are costly devices and the OEO conversion can affect the efficiency of optical networks we need to use the regenerators efficiently and effectively. In this paper, we study the problem of placing the minimum number of regenerators to accommodate all requests with the consideration of physical impairments. We first propose a novel ILP formulation for an optimal solution and a benchmark for this problem. We then provide an effective heuristic for largesized WDM networks. Simulation results show that our schemes have good performance in terms of network design and running time.

Self-healing sensor networks with distributed decision making

Sensor random locations and sensor failures can cause coverage holes, routing voids and disconnections and thus degrade sensor network performance. In this paper, we present a self-healing approach that utilises a few mobile sensors to deal with the problems. A mobile sensor can move to an area with a coverage hole or routing void and significantly improve network performance. We design distributed algorithms to detect coverage holes, estimate hole size and repair holes. A mobile sensor may receive multiple repair requests from different areas and it may not have complete information of the network. We present a fuzzy-logic-based distributed decision-making algorithm for mobile sensors. Extensive simulations demonstrate the good performance of proposed algorithms.