Underwater Data Collection Using Robotic Sensor Networks

We examine the problem of utilizing an autonomous underwater vehicle (AUV) to collect data from an underwater sensor network. The sensors in the network are equipped with acoustic modems that provide noisy, range-limited communication. The AUV must plan a path that maximizes the information collected while minimizing travel time or fuel expenditure. We propose AUV path planning methods that extend algorithms for variants of the Traveling Salesperson Problem (TSP). While executing a path, the AUV can improve performance by communicating with multiple nodes in the network at once. Such multi-node communication requires a scheduling protocol that is robust to channel variations and interference. To this end, we examine two multiple access protocols for the underwater data collection scenario, one based on deterministic access and another based on random access. We compare the proposed algorithms to baseline strategies through simulated experiments that utilize models derived from experimental test data. Our results demonstrate that properly designed communication models and scheduling protocols are essential for choosing the appropriate path planning algorithms for data collection.United States. Office of Naval Research (ONR N00014-09-1-0700)United States. Office of Naval Research (ONR N00014-07-1-00738)National Science Foundation (U.S.) (NSF 0831728)National Science Foundation (U.S.) (NSF CCR-0120778)National Science Foundation (U.S.) (NSF CNS-1035866

Algoritmos de aproximação para problemas de roteamento e conectividade com múltiplas funções de distância

Orientador: Lehilton Lelis Chaves PedrosaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Nesta dissertação, estudamos algumas generalizações de problemas clássicos de roteamento e conectividade cujas instâncias são compostas por um grafo completo e múltiplas funções de distância. Por exemplo, existe o Problema do Caixeiro Alugador (CaRS), no qual um viajante deseja visitar um conjunto de cidades alugando um ou mais carros disponíveis. Cada carro tem uma função de distância e uma taxa de retorno ao local do aluguel. CaRS é uma generalização do Problema do Caixeiro Viajante (TSP). Nós lidamos com esses problemas usando algoritmos de aproximação, que são algoritmos eficientes que produzem soluções com garantia de qualidade. Neste trabalho, são apresentadas duas abordagens, uma baseada em uma redução linear que preserva o fator de aproximação e outra baseada na construção de instâncias de dois problemas distintos. Os problemas considerados são o Steiner TSP, o Problema do Passeio com Coleta de Prêmios e o Problema da Floresta Restrita. Generalizamos cada um desses problemas considerando múltiplas funções de distância e, para cada um deles, apresentamos um algoritmo de aproximação com fator O(logn), onde n é o número de vértices (cidades). Essas aproximações são assintoticamente ótimas, já que não há algoritmos com fator o(log n), a não ser que P = NPAbstract: In this dissertation, we study some generalizations of classical routing and connectivity problems whose instances are composed of a complete graph and multiple distance functions. As an example, there is the Traveling Car Renter Problem (CaRS) in which a traveler wants to visit a set of cities by renting one or more available cars. Each car is associated to a distance function and a service fee to return to the rental location. CaRS is a generalization of the Traveling Salesman Problem (TSP). We deal with these problems using approximation algorithms which are efficient algorithms that produce solutions with quality guarantee. In this work, two approaches are presented, one based on a linear reduction that preserves the approximation factor and the other based on the construction of instances of two distinct problems. The studied problems are the Steiner TSP, the Profitable Tour Problem, and the Constrained Forest Problem. We generalize these problems by considering multiple distance functions and, for each of them, we present an O(log n)-approximation algorithm, where n is the number of vertices (cities). The factor is asymptotically optimal, since there is no approximation algorithm with factor o(log n) unless P = NPMestradoCiência da ComputaçãoMestra em Ciência da Computação001CAPE

The online Prize-Collecting Traveling Salesman Problem

We study the online version of the Prize-Collecting Traveling Salesman Problem (PCTSP), a generalization of the Traveling Salesman Problem (TSP). In the TSP, the salesman has to visit a set of cities while minimizing the length of the overall tour. In the PCTSP, each city has a given weight and penalty, and the goal is to collect a given quota of the weights of the cities while minimizing the length of the tour plus the penalties of the cities not in the tour. In the online version, cities are disclosed over time. We give a 7/3-competitive algorithm for the problem, which compares with a lower bound of 2 on the competitive ratio of any deterministic algorithm. We also show how our approach can be combined with an approximation algorithm in order to obtain an O (1)-competitive algorithm that runs in polynomial time. © 2008 Elsevier B.V. All rights reserved

Placement and motion planning algorithms for robotic sensing systems

University of Minnesota Ph.D. dissertation. October 2014. Major: Computer Science. Advisor: Prof. Ibrahim Volkan Isler. I computer file (PDF); xxiii, 226 pages.Recent technological advances are making it possible to build teams of sensors and robots that can sense data from hard-to-reach places at unprecedented spatio-temporal scales. Robotic sensing systems hold the potential to revolutionize a diverse collection of applications such as agriculture, environmental monitoring, climate studies, security and surveillance in the near future. In order to make full use of this technology, it is crucial to complement it with efficient algorithms that plan for the sensing in these systems. In this dissertation, we develop new sensor planning algorithms and present prototype robotic sensing systems.In the first part of this dissertation, we study two problems on placing stationary sensors to cover an environment. Our objective is to place the fewest number of sensors required to ensure that every point in the environment is covered. In the first problem, we say a point is covered if it is seen by sensors from all orientations. The environment is represented as a polygon and the sensors are modeled as omnidirectional cameras. Our formulation, which builds on the well-known art gallery problem, is motivated by practical applications such as visual inspection and video-conferencing where seeing objects from all sides is crucial. In the second problem, we study how to deploy bearing sensors in order to localize a target in the environment. The sensors measure noisy bearings towards the target which can be combined to localize the target. The uncertainty in localization is a function of the placement of the sensors relative to the target. For both problems we present (i) lower bounds on the number of sensors required for an optimal algorithm, and (ii) algorithms to place at most a constant times the optimal number of sensors. In the second part of this dissertation, we study motion planning problems for mobile sensors. We start by investigating how to plan the motion of a team of aerial robots tasked with tracking targets that are moving on the ground. We then study various coverage problems that arise in two environmental monitoring applications: using robotic boats to monitor radio-tagged invasive fish in lakes, and using ground and aerial robots for data collection in precision agriculture. We formulate the coverage problems based on constraints observed in practice. We also present the design of prototype robotic systems for these applications. In the final problem, we investigate how to optimize the low-level motion of the robots to minimize their energy consumption and extend the system lifetime.This dissertation makes progress towards building robotic sensing systems along two directions. We present algorithms with strong theoretical performance guarantees, often by proving that our algorithms are optimal or that their costs are at most a constant factor away from the optimal values. We also demonstrate the feasibility and applicability of our results through system implementation and with results from simulations and extensive field experiments

Models and Algorithms for Online Server Routing

Combinatorial optimization is the discipline that studies problems in which one seeks to minimize or maximize an objective function by appropriately choosing the values of some variables from within an allowed finite set. In a typical combinatorial optimization problem, the feasibility of a solution can be efficiently verified, but the number of feasible solutions is so large that an exhaustive search of an optimal solution is doomed to failure. Thus, efficient combinatorial optimization algorithms need to exploit the structure of the problem being solved. While the classical approach to a combinatorial optimization problem is to assume that all relevant data are available before a solution method is applied, it has recently become more and more evident that in many applications data arrive step by step and a partial solution needs to be maintained at every step. Typical examples of online problems are the scheduling of processes in an operating system, or the trade of stocks in a financial market. In these applications the data is arriving over time and the algorithm that solves the problem has to be online, meaning that it has to keep at every time a solution that has been produced without knowledge of future data. It is quite clear that, because of this lack of information about the future, an online algorithm will not in general be able to produce the optimal solution. Competitive analysis is a theoretical framework that allows to quantify the worst-case suboptimality of the solutions found by an online algorithm. An online algorithm is called competitive if it produces solutions whose cost is always within a constant factor of the optimal solution. In this thesis we study competitive algorithms for server routing problems. In a server routing problem, one or more servers move in a metric space in order to visit some requested points in the space. The objective is to minimize some function of the movement of the servers. An important example is the traveling salesman problem, in which a salesman has to find a round-trip tour through a set of cities in order to minimize the total length of the tour. We consider online versions of this and other server routing problem, in which the points to be visited are released over time. After giving a brief introduction to the field of online optimization in the first chapter of this thesis, in Chapter 2 we review the basic complexity results for offline server routing problems, we introduce formally the online server routing frame- work and we survey the state of the art. We show the basic proof techniques and we discuss several attempts in the literature to extend the basic competitive analysis setting. In Chapter 3, we consider the online asymmetric traveling salesman problem from the point of view of competitive analysis. For the homing version, where the server has to return to its starting point, we give an algorithm that has the best possible competitive ratio. We also consider the nomadic version (where returning to the starting point is not mandatory) and prove that it does not admit constant competitive algorithms. However, for the nomadic version we prove a competitive ratio as a function of the amount of asymmetry of the space. We also consider the competitiveness of zealous algorithms, in which, intuitively, the server is not allowed to remain idle when there are outstanding requests. Finally we discuss the issue of polynomial time online algorithms for the problem. In Chapter 4, we study the online prize-collecting traveling salesman problem. After discussing the approximation ratio of the offline version, we give a 7/3-competitive algorithm. We also consider the special case of the halfline as the metric space, for which we prove lower and upper bounds of 1.89 and 2, respectively, on the competitive ratio of deterministic algorithms. In Chapter 5, we consider the online nomadic traveling salesman and the online traveling repairman with k servers. We give competitive algorithms whose competitive ratios match the ones for the single server variants. For the special case of the real line, we prove the existence of algorithms with competitive ratio 1 + O((log k)/k), meaning that we can approach the optimal cost as k grows. We also show that this phenomenon is limited to the one dimensional case, since already in the Euclidean plane, we prove a lower bound of 4/3 for the online nomadic TSP and of 5/4 for the online TRP independently of the number of servers. Finally, we give resource augmentation results that are asymptotically best possible as the number of online servers grows beyond the number of offline servers. In Chapter 6, in order to address the limits of competitive analysis, we introduce a new model for online server routing based on adversarial queueing theory. The model addresses the stability of online algorithms that are continuously operating. We call an online algorithm stable if there exists an upper bound on the number of unserved requests at any time that does not depend on the time the system has been running. We consider a number of natural algorithms in this model and we prove the existence of algorithms that are stable and such that the maximum flow time of a request also does not depend on the time the system has been running