Routing Unmanned Vehicles in GPS-Denied Environments

Most of the routing algorithms for unmanned vehicles, that arise in data gathering and monitoring applications in the literature, rely on the Global Positioning System (GPS) information for localization. However, disruption of GPS signals either intentionally or unintentionally could potentially render these algorithms not applicable. In this article, we present a novel method to address this difficulty by combining methods from cooperative localization and routing. In particular, the article formulates a fundamental combinatorial optimization problem to plan routes for an unmanned vehicle in a GPS-restricted environment while enabling localization for the vehicle. We also develop algorithms to compute optimal paths for the vehicle using the proposed formulation. Extensive simulation results are also presented to corroborate the effectiveness and performance of the proposed formulation and algorithms.Comment: Publised in International Conference on Umanned Aerial System

Development and evaluation of localization techniques for vehicular ad hoc networks and intelligent transportation systems

Orientadores: Leandro Aparecido Villas, Daniel Ludovico GuidoniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Devido aos diversos problemas nos sistemas de tráfego ocasionados pela evolução dos grandes centros urbanos, existe o campo de estudo dos Sistemas de Transportes Inteligentes (Intelligent Transportation Systems, ITS), que visa prover metodologias de comunicação, processamento e armazenamento de dados voltados para o segmento de transportes e comutação de pessoas nas cidades. Neste contexto, o advento das tecnologias de comunicação sem fio, sobretudo das tecnologias de comunicação sem fio dedicadas e de curto alcance, culminaram no surgimento do padrão de comunicação sem fio IEEE 802.11p para as redes veiculares (Vehicular Ad Hoc Netowrks, VANETs). Nos últimos anos, uma miríade de protocolos, aplicações e serviços vêm sendo desenvolvidos com os mais diversos objetivos, variando desde conforto a segurança. Muitos destes serviços confiam em algum sistema de localização, e necessitam de diferentes níveis de acurácia para seu pleno funcionamento. A solução imediata para localização em VANETS e ITS são os Sistemas de Navegação Global via Satélite (Global Navigation Satellite System, GNSS). No entanto, os sistemas GNSS sofrem problemas de inacurácia e indisponibilidade em zonas urbanas densas, rodovias multinível e túneis, o que representa um desafio para os protocolos, aplicações e serviços que confiam em localização. Com esta motivação, primeiramente foi realizada uma caracterização dos problemas de inacurácia e indisponibilidade dos sistemas GPS a partir de datasets reais. Foram selecionadas regiões no entorno de túneis. Uma vez que os nós da rede veicular são dotados de capacidade de comunicação sem-fio, processamento e armazenamento. Foram desenvolvidas e avaliadas as técnicas de localização Dead Reckoning e uma abordagem Cooperative Positioning onde os veículos compartilham suas estimativas de localização por meio da rede veicular com o objetivo de melhorar suas estimativas de localizações. As situações de indisponibilidade caracterizadas nos datasets foram reproduzidas em ambiente de simulação para validação das soluções de localização propostas. Resultados de simulação apresentam um ganho médio de 60% a 80% da solução Dead Reckoning em termos do Erro Médio Quadrático (Root Mean Square Erro, RMSE), se comparados com os resultados da solução stand alone GPS. Os resultados da solução Cooperative Positioning apresentam um ganho médio entre 80% e 92% no RMSE em relação a solução GPS stand alone, e entre 23% a 74% em relação a solução Dead Reckoning. Além disso, as soluções conseguem cobrir 100% das zonas de indisponibilidade do GPS nos cenários avaliadosAbstract: Due to the many problems in the traffic systems caused by the evolution of the large urban centers, there is the field of study of Intelligent Transportation Systems (ITS), which aims to provide communication, data processing and storage methodologies for the transport of people, assets and services in the cities. In this context, the advent of the wireless communications technologies especially the Dedicated Short Range Communications (DSRC), culminated in the development of the IEEE 802.11p standard for Vehicular Ad Hoc Networks (VANETs). In recent years, a myriad of protocols, applications, and services have been developed with a wide range of objectives, ranging from comfort to security. Many of these services rely on some location system, and require different levels of accuracy for their full operation. The Global Navigation Satellite Systems (GNSSs) are an off-the-shelf solution for localization in VANETS and ITS. However, GNSS systems suffer from problems of inaccuracy and unavailability in dense urban areas, multilevel roads and tunnels, posing a challenge for protocols, applications and services that rely on localization. With this motivation, we first carried out a characterization of the problems of inaccuracy and unavailability of GPS systems from real datasets. Regions were selected around tunnels. Since the nodes of the vehicular network are endowed with wireless communication, processing and storage capacbilities. A Dead Reckoning technique and a Cooperative Positioning approach were developed and evaluated. Vehicles share their location estimates using the vehicular network in order to improve their locations. The unavailability situations characterized in the data sets were reproduced in a simulation environment to validate the proposed localization solutions. Simulation results show an average gain of 60% to 80% of the Dead Reckoning solution in terms of RMSE, when compared to the results of the stand alone GPS solution. The results of the Cooperative Positioning solution show an average gain between 80% and 92% in the RMSE compared to the stand alone GPS solution, and between 23% and 74% in relation to the Dead Reckoning solution. In addition, the solutions can support 100% of the GPS unavailability zones on the evaluated cenariosMestradoCiência da ComputaçãoMestre em Ciência da Computação132244/2016-0CNP

Neural Network based Robot 3D Mapping and Navigation using Depth Image Camera

Robotics research has been developing rapidly in the past decade. However, in order to bring robots into household or office environments and cooperate well with humans, it is still required more research works. One of the main problems is robot localization and navigation. To be able to accomplish its missions, the mobile robot needs to solve problems of localizing itself in the environment, finding the best path and navigate to the goal. The navigation methods can be categorized into map-based navigation and map-less navigation. In this research we propose a method based on neural networks, using a depth image camera to solve the robot navigation problem. By using a depth image camera, the surrounding environment can be recognized regardless of the lighting conditions. A neural network-based approach is fast enough for robot navigation in real-time which is important to develop the full autonomous robots.In our method, mapping and annotating of the surrounding environment is done by the robot using a Feed-Forward Neural Network and a CNN network. The 3D map not only contains the geometric information of the environments but also their semantic contents. The semantic contents are important for robots to accomplish their tasks. For instance, consider the task “Go to cabinet to take a medicine”. The robot needs to know the position of the cabinet and medicine which is not supplied by solely the geometrical map. A Feed-Forward Neural Network is trained to convert the depth information from depth images into 3D points in real-world coordination. A CNN network is trained to segment the image into classes. By combining the two neural networks, the objects in the environment are segmented and their positions are determined.We implemented the proposed method using the mobile humanoid robot. Initially, the robot moves in the environment and build the 3D map with objects placed in their positions. Then, the robot utilizes the developed 3D map for goal-directed navigation.The experimental results show good performance in terms of the 3D map accuracy and robot navigation. Most of the objects in the working environments are classified by the trained CNN. Un-recognized objects are classified by Feed-Forward Neural Network. As a result, the generated maps reflected exactly working environments and can be applied for robots to safely navigate in them. The 3D geometric maps can be generated regardless of the lighting conditions. The proposed localization method is robust even in texture-less environments which are the toughest environments in the field of vision-based localization.博士(工学)法政大学 (Hosei University