Decision-Making for Search and Classification using Multiple Autonomous Vehicles over Large-Scale Domains

This dissertation focuses on real-time decision-making for large-scale domain search and object classification using Multiple Autonomous Vehicles (MAV). In recent years, MAV systems have attracted considerable attention and have been widely utilized. Of particular interest is their application to search and classification under limited sensory capabilities. Since search requires sensor mobility and classification requires a sensor to stay within the vicinity of an object, search and classification are two competing tasks. Therefore, there is a need to develop real-time sensor allocation decision-making strategies to guarantee task accomplishment. These decisions are especially crucial when the domain is much larger than the field-of-view of a sensor, or when the number of objects to be found and classified is much larger than that of available sensors. In this work, the search problem is formulated as a coverage control problem, which aims at collecting enough data at every point within the domain to construct an awareness map. The object classification problem seeks to satisfactorily categorize the property of each found object of interest. The decision-making strategies include both sensor allocation decisions and vehicle motion control. The awareness-, Bayesian-, and risk-based decision-making strategies are developed in sequence. The awareness-based approach is developed under a deterministic framework, while the latter two are developed under a probabilistic framework where uncertainty in sensor measurement is taken into account. The risk-based decision-making strategy also analyzes the effect of measurement cost. It is further extended to an integrated detection and estimation problem with applications in optimal sensor management. Simulation-based studies are performed to confirm the effectiveness of the proposed algorithms

Near-Optimal Deviation-Proof Medium Access Control Designs in Wireless Networks

Distributed medium access control (MAC) protocols are essential for the proliferation of low cost, decentralized wireless local area networks (WLANs). Most MAC protocols are designed with the presumption that nodes comply with prescribed rules. However, selfish nodes have natural motives to manipulate protocols in order to improve their own performance. This often degrades the performance of other nodes as well as that of the overall system. In this work, we propose a class of protocols that limit the performance gain which nodes can obtain through selfish manipulation while incurring only a small efficiency loss. The proposed protocols are based on the idea of a review strategy, with which nodes collect signals about the actions of other nodes over a period of time, use a statistical test to infer whether or not other nodes are following the prescribed protocol, and trigger a punishment if a departure from the protocol is perceived. We consider the cases of private and public signals and provide analytical and numerical results to demonstrate the properties of the proposed protocols.Comment: 14 double-column pages, submitted to ACM/IEEE Trans Networkin

Assessing Positional Accuracy and Correcting Point Data for Digital Soil Mapping at Varying Scales

Accuracy, timeliness, and the effect of scale of soil maps are rarely assessed. The recent increase in the use of GIS technologies and modelling software in natural resources and land management, has increased the demand for soil information at a finer resolution worldwide. Most of the world\u27s developing countries rely on soils information at a scale that is too coarse for practical planning, and have obstacles impeding collection of new data, such as civil war and a lack of collection resources. The United States has an exhaustive collection of soils data at a fine scale. However, its location information is replete with errors and inconsistencies which, if unaccounted for, can affect predictive model estimates. An integrated digital soil mapping methodology is necessary to extract the wealth of knowledge stored in soil survey data for building detailed soil maps and for assessing the positional accuracy of soil pedon data. Two studies were conducted using public data contained in the U.S. Soil Survey databases. The first study tested the development of an accurate regional-scale digital soil class map by combining new elevation data and satellite imagery. As a result, a model design was created that may be applied in countries with limited soil data. In the second study, several models were developed to assess the locational accuracy of the U.S. Soil Survey pedon points for Indiana. The study resulted in the creation of a more detailed Public Land Survey System grid, as well as several ArcGIS tools to assign a margin of error to existing soil pedon point locations, which separately or together can be adopted on a national scale