Catálogo de publicaciones - libros

We present a new approach to solving adaptive scheduling problems in decentralized systems, based on the concept of nearest-neighbor negotiations and the idea of a consensus variable. Exploiting some recent extensions to existing results for single consensus variables, the adaptive scheduling problem is solved by choosing task timings as the consensus variables in the system. This application is illustrated via the example of a synchronized strike mission. The chapter concludes with a discussion of future research directions on this topic.

We consider the effects of cueing in a cooperative search mission that involves several autonomous agents. Two scenarios are discussed: one in which the search is conducted by a number of identical search-and-engage vehicles, and one where these vehicles are assisted by a search-only (reconnaissance) asset. The cooperation between the autonomous agents is facilitated via cueing, i.e. the information transmitted to the agents by a searcher that has just detected a target. The effect of cueing on the target detection probability is derived from first principles using a Markov chain analysis. Exact solutions to Kolmogorov-type differential equations are presented, and existence of an upper bound on the benefit of cueing is demonstrated.

This chapter presents a search and rescue scenario modeled as a discrete-state, continuous-time Markov process. In this scenario, there are two vehicle types: search-only vehicles capable of searching for persons in distress, but not engaging or rescuing them, and search-and-engage vehicles with the capability both to search and to rescue. All vehicles have two-way communication with other vehicles. Both vehicle types can search independently, but information provided by other vehicles improves their detection capability. We develop a Markov model and use matrix exponentiation to numerically determine the transient state probabilities for the system. We use as a measure of effectiveness the time required for at least two search-and-engage vehicles to arrive on scene with a threshold probability. We then analyze the trade-space between cueing capability and vehicle detection rates.

Coordination of large numbers of unmanned aerial vehicles is difficult due to the limited communication bandwidth available to maintain cohesive activity in a dynamic, often hostile and unpredictable environment. We have developed an integrated coordination algorithm based on the movement of around a network of vehicles. Possession of a token represents exclusive access to the task or resource represented by the token or exclusive ability to propagate the information represented by the token. The movement of tokens is governed by a local decision theoretic model that determines what to do with the tokens in order to maximize expected utility. The result is effective coordination between large numbers of UAVs with very little communication. However, the overall movement of tokens can be very complex and, since it relies on heuristics, configuration parameters need to be tuned for a specific scenario or preferences. We have developed a neural network model of the relationship between configuration and environment parameters and performance, that an operator uses to rapidly configure a team or even reconfigure the team online, as the environment changes.

In this work, two novel optimization-based strategies for multi-robot coordination are presented. The proposed algorithms employ a () version of a Newton-type approach for solving the underlying optimization problem. Both methods can generate control inputs for vehicles with nonholonomic constraints moving in a configuration space cluttered by obstacles. Obstacle- and inter-collision constraints are incorporated into the optimization problem by using interior and exterior penalty function approaches. Moreover, convergence of the algorithms is studied with and without the presence of obstacles in the environment. Simulation results verify the validity of the proposed methodology.

Multi-vehicle systems are naturally encountered in civil and military applications. Cooperation amongst individual “miniaturized” vehicles allows for flexibility to accomplish missions that a single large vehicle may not readily be able to accomplish. While accomplishing a mission, motion planning algorithms are required to efficiently utilize a common resource (such as the total fuel in the collection of vehicles) or to minimize a collective cost function (such as the maximum time taken by the vehicles to reach their intended destination). The objective of this chapter is to present a constant factor approximation algorithm for planning the path of each vehicle in a collection of vehicles, where the motion of each vehicle must satisfy yaw rate constraints.

We extend our earlier work, Golodnikov [] and Golodnikov [], by estimating the entire probability distributions for the impact toughness characteristic of steels, as measured by Charpy V-Notch (CVN) at −84°C. Quantile regression, constrained to produce monotone quantile function and unimodal density function estimates, is used to construct the empirical quantiles as a function of various alloy chemical composition and processing variables. The estimated quantiles are used to produce an estimate of the underlying probability density function, rendered in the form of a histogram. The resulting CVN distributions are much more informative for alloy design than singular test data. Using the distributions to make decisions for selecting better alloys should lead to a more effective and comprehensive approach than the one based on the minimum value from a multiple of the three test, as is commonly practiced in the industry.

Ad hoc networks have been used in the last few years to provide communications means among agents that need to accomplish common goals. Due to the importance of communication for the success of such missions, we study the problem of maximizing communication among a set of agents. As a practical tool to solve such problems, we introduce a one-pass randomized algorithm that maximizes the total communication, as measured by the proposed objective function. Agents in this problem are routed along the edges of a graph, connecting their individual starting nodes to their respective destination nodes. This problem, known as the , is known to be NP-hard. We present a new heuristic and motivate the need for more advanced methods for the solution of this problem. In particular, we describe 1) a construction algorithm and 2) a local improvement method for maximizing communication. Computational results for the proposed approach are provided, showing that instances of realistic size can be efficiently solved by the algorithm.

Nonlinear properties of a magnetic levitation system and an algorithm of a probe stability are studied. The phenomenon, in which a macroscopic superconducting ring chaotically and magnetically levitates, is considered. A nonlinear control scheme of a dynamic type is proposed for the control of a magnetic levitation system. The proposed controller guarantees the asymptotic regulation of the system states to their desired values. We found that if a non-linear feedback is used then the probe chaotically moves near an equilibrium state. An optimization approach for selection of optimum parameters is discussed.

In this chapter, we are concerned with decision making methods for dynamic systems under uncertainty. We consider expected value optimization of stochastic systems and worst-case robust strategies. Stochastic decision-making involves uncertainty and consequently risk. An important tool to address the inherent error for forecasting uncertainty is worst-case analysis. From the risk management point of view, minimax yields the best strategy determined simultaneously with the worst state of the underlying system. Worst-case analysis is a robust framework for decisions under uncertainty as the actual performance of the decision has a noninferiority property. The significance of robust strategies is increasingly recognized as attitudes towards risk evolve in diverse areas. We present worst-case approach to macroeconomics policy making and financial portfolio management.