A Novel Approach To Intelligent Navigation Of A Mobile Robot In A Dynamic And Cluttered Indoor Environment

The need and rationale for improved solutions to indoor robot navigation is increasingly driven by the influx of domestic and industrial mobile robots into the market. This research has developed and implemented a novel navigation technique for a mobile robot operating in a cluttered and dynamic indoor environment. It divides the indoor navigation problem into three distinct but interrelated parts, namely, localization, mapping and path planning. The localization part has been addressed using dead-reckoning (odometry). A least squares numerical approach has been used to calibrate the odometer parameters to minimize the effect of systematic errors on the performance, and an intermittent resetting technique, which employs RFID tags placed at known locations in the indoor environment in conjunction with door-markers, has been developed and implemented to mitigate the errors remaining after the calibration. A mapping technique that employs a laser measurement sensor as the main exteroceptive sensor has been developed and implemented for building a binary occupancy grid map of the environment. A-r-Star pathfinder, a new path planning algorithm that is capable of high performance both in cluttered and sparse environments, has been developed and implemented. Its properties, challenges, and solutions to those challenges have also been highlighted in this research. An incremental version of the A-r-Star has been developed to handle dynamic environments. Simulation experiments highlighting properties and performance of the individual components have been developed and executed using MATLAB. A prototype world has been built using the WebotsTM robotic prototyping and 3-D simulation software. An integrated version of the system comprising the localization, mapping and path planning techniques has been executed in this prototype workspace to produce validation results

Adaptive Learning Terrain Estimation for Unmanned Aerial Vehicle Applications

For the past decade, terrain mapping research has focused on ground robots using occupancy grids and tree-like data structures, like Octomap and Quadtrees. Since flight vehicles have different constraints, ground-based terrain mapping research may not be directly applicable to the aerospace industry. To address this issue, Adaptive Learning Terrain Estimation algorithms have been developed with an aim towards aerospace applications. This thesis develops and tests Adaptive Learning Terrain Estimation algorithms using a custom test benchmark on representative aerospace cases: autonomous UAV landing and UAV flight through 3D urban environments. The fundamental objective of this thesis is to investigate the use of Adaptive Learning Terrain Estimation algorithms for aerospace applications and compare their performance to commonly used mapping techniques such as Quadtree and Octomap. To test the algorithms, point clouds were collected and registered in simulation and real environments. Then, the Adaptive Learning, Quadtree, and Octomap algorithms were applied to the data sets, both in real-time and offline. Finally, metrics of map size, accuracy, and running time were developed and implemented to quantify and compare the performance of the algorithms. The results show that Quadtree yields the computationally lightest maps, but it is not suitable for real-time implementation due to its lack of recursiveness. Adaptive Learning maps are computationally efficient due to the use of multiresolution grids. Octomap yields the most detailed maps, but it produces a high computational load. The results of the research show that Adaptive Learning algorithms have significant potential for real-time implementation in aerospace applications. Their low memory load and variable-sized grids make them viable candidates for future research and development

Development of an autonomous mobile robot with planning and location in a structured environment

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáWith the advance of technology mobile robots have been increasingly applied in the industry, performing repetitive work with high performance, and in environments that pose risks to human health. The present work plans and develops a mobile robot platform for the micromouse competition. The micromouse consists of a small autonomous mobile robot that, when placed in an unknown labyrinth, is able to map it, search for the best path between the starting point and the goal and travel it in the shortest possible time. To accomplish these tasks, the robot must be able to self-locate, map the maze as it traverses it and plan paths based on the map obtained. The developed self-localization method is based on the odometry, the laser sensors present in the robot and on a previous knowledge of the start point and the configuration of the environment. Several methodologies of locomotion in unknown environment and route planning are analyzed in order to obtain the combination with the best performance. In order to verify the results, the present work is developed in real environment, in 3D simulation and also with a hardware in the loop capability. Labyrinths from previous competitions are used as basis for comparing methodologies and validating results. At the end it presents the algorithm capable of fulfilling all the requirements of the micromouse competition together with the results of its evaluation run.Com o avanço da tecnologia, os robôs móveis têm sido cada vez mais aplicados na indústria, realizando trabalhos repetitivos com alto desempenho e em ambientes que expõem riscos à saúde humana. O presente trabalho planeja e desenvolve um robô móvel para a competição micromouse. O micromouse consiste em um pequeno robô autônomo que, ao ser colocado em um labirinto desconhecido, é capaz de mapeá-lo, procurar o melhor caminho entre o ponto de partida e o objetivo, e percorrê-lo no menor tempo possível. Para realizar estas tarefas, o robô deve ser capaz de se auto-localizar, mapear o labirinto enquanto o percorre e planejar caminhos com base no mapa obtido. O método de auto-localização desenvolvido baseia-se na odometria, nos sensores a laser presentes no robô e em um prévio conhecimento do ponto de início e da configuração do ambiente. Diversas metodologias de locomoção em ambiente desconhecido e planejamento de rotas são analisadas buscando-se obter a combinação com o melhor desempenho. Para averiguação de resultados o presente trabalho desenvolve-se em ambiente real e em simulação 3D com hardware in the loop. Labirintos de competições anteriores são utilizados de base para o comparativo de metodologias e validação de resultados. Ao final apresenta-se o algoritmo capaz de cumprir todas as exigências da competição micromouse juntamente com os resultados em sua corrida de avaliação

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas

Hybrid PSO-PWL-Dijkstra approach for path planning of non holonomic platforms in dense contexts

Planning is an essential capability for autonomous robots. Many applications impose a diversity of constraints and traversing costs in addition to the usually considered requirement of obstacle avoidance. In applications such as route planning, the use of dense properties is convenient as these describe the terrain and other aspects of the context of operation more rigorously and are usually the result of a concurrent mapping and learning process. Unfortunately, planning for a platform with more than three degrees of freedom can be computationally expensive, particularly if the application requires the platform to optimally deal with a thorough description of the terrain. The objective of this thesis is to develop and demonstrate an efficient path planning algorithm based on dynamic programming. The goal is to compute paths for ground vehicles with and without trailers, that minimise a specified cost-to-go while taking into account dynamic constraints of the vehicle and dense properties of the environment. The proposed approach utilises a Quadtree Piece-Wise Linear (QT-PWL) approximation to describe the environment in a low dimensional subspace and later uses a particle approach to introduce the dynamic constraints of the vehicle and to smooth the path in the full dimensional configuration space. This implies that the optimisation process can exploit the QT-PWL partition. Many usual contexts of operation of autonomous platforms have cluttered spaces and large regions where the dense properties are smooth; therefore, the QT-PWL partition is able to represent the context in a fraction of cells that would be needed by a homogeneous grid. The proposed methodology includes adaptations to both algorithms to achieve higher efficiency of the computational cost and optimality of the planned path. In order to demonstrate the capabilities of the algorithm, an idealized test case is presented and discussed. The case for a car and a tractor with multiple trailers is presented. A real path planning example is presented in addition to the synthetic experiments. Finally, the experiments and results are analysed and conclusions and directions for possible future work are presented

GUI3DXBot: Una herramienta software interactiva para un robot móvil guía

Nowadays, mobile robots begin to appear in public places. To do these tasks properly, mobile robots must interact with humans. This paper presents the development of GUI3DXBot, a software tool for a tour-guide mobile robot. The paper focuses on the development of different software modules needed to guide users in an office building. In this context, GUI3DXBot is a server-client application, where the server side runs into the robot, and the client side runs into a 10-inch Android tablet. The GUI3DXBot server side is in charge of performing the perception, localization-mapping, and path planning tasks. The GUI3DXBot client side implements the human-robot interface that allows users requesting-canceling a tour-guide service, showing robot localization in the map, interacting with users, and tele-operating the robot in case of emergency. The contributions of this paper are twofold: it proposes a software modules design to guide users in an office building, and the whole robot system was well integrated and fully tested. GUI3DXBot were tested using software integration and field tests. The field tests were performed over a two-week period, and a survey to users was conducted. The survey results show that users think GUI3DXBot is friendly and intuitive, the goal selection was very easy, the interactive messages were very easy to understand, 90% of users found useful the robot icon on the map, users found useful drawing the path on the map, 90% of users found useful the local-global map view, and the guidance experience was very satisfactory (70%) and satisfactory (30%).Actualmente, los robots móviles inician a aparecer en lugares públicos. Para realizar estas tareas adecuadamente, los robots móviles deben interactuar con humanos. Este artículo presenta GUI3DXBot, un aplicativo para un robot móvil guía. Este artículo se enfoca en el desarrollo de los diferentes módulos software necesarios para guiar a usuarios en un edificio de oficinas. GUI3DXBot es una aplicación cliente-servidor, donde el lado del servidor se ejecuta en el robot, y el lado del cliente se ejecuta en una tableta de 10 pulgadas Android. El lado servidor de GUI3DXBot está a cargo de la percepción, localización-mapeo y planificación de rutas. El lado cliente de GUI3DXBot implementa la interfaz humano-robot que permite a los usuarios solicitar-cancelar un servicio de guía, mostrar la localización del robot en el mapa, interactuar con los usuarios, y tele-operar el robot en caso de emergencia. Las contribuciones de este artículo son dos: se propone un diseño de módulos software para guiar a usuarios en un edificio de oficinas, y que todo el sistema robótico está bien integrado y completamente probado. GUI3DXBot fue validada usando pruebas de integración y de campo. Las pruebas de campo fueron realizadas en un periodo de 2 semanas, y una encuesta a los usuarios fue llevada a cabo. Los resultados de la encuesta mostraron que los usuarios piensan que GUI3DXBot es amigable e intuitiva, la selección de metas fue fácil, pudieron entender los mensajes de interacción, 90% de los usuarios encontraron útil el ícono del robot sobre el mapa, encontraron útil dibujar la ruta planeada en el mapa, 90% de los usuarios encontraron útil la vista local-global del mapa, y la experiencia de guía fue muy satisfactoria (70%) y satisfactoria (30%)

Improving Library Material Shelving Time By Implementing An Autonomous Book Truck

DissertationThe prompt shelving of returned library books is an important task in any traditional library. To help speed up the shelving process, this dissertation proposes an automated book truck capable of moving returned library books from the return desk back to the shelves. By making use of the design and creation research methodology, software algorithms, sensors and robotic hardware are evaluated and then selected to construct an autonomous book truck. It is determined that an autonomous book truck should consist of a robotic body that has the same footprint as an average human. Furthermore, the sensor skirt should consist of at least a LIDAR or equivalent sensor to be used for obstacle avoidance and that sonar sensors should be used for localisation. A simulator is created to test the selected components with the simulation data suggesting that shelving time – and therefore the dead time of returned books – is reduced by a significant factor. The research also provides a possible prototype which can be used for further development.