Software for a Service Robot

Service robots are becoming more commonplace every year due to advances in artificial intelligence, substituting humans in increasingly more complex tasks. By having an autonomous and competent service robot performing routinely tasks instead of its human owners, their productivity increases. The search for better service robots has led to the creation of competitions where such robots are tested and the state of the art technology is pushed further. Socialab acquired a Turtlebot2 robot to serve people around the university campus and one day participate in such competitions. With hopes of achieving these goals, the laboratory has proposed a variety of projects over the years, each adding new layers offunctionality to the robot. With each studentthathas tackled their respective project,thedeveloped softwarehasbeencontinuously stacking. However, each completed project has remained separate from each other andhasn’t been used ever since. Hence,the developed software is being wasted. Therefore, it is imperative to integrate all the available software into the robot. Yet, as new projects are proposed, the problem of scattered software can reoccur after the integration of the currently available ones. Furthermore, with more functionality that is developed, the harder and longer it takes to complete their integration. To preventthis entirely, itis necessary to create structural software that eases the development of new functionality as well as its integration with the current software. To achieve this, a class was developed which is responsible for controlling the execution of all processes running in the robot, of which the different software depends on. Additionally, research was done on multiple competitions to identify the most commonly required functionality traits, which we refer to as modules. Afterwards, an implementation of each of these modules was developed. Because of their universality, their implementation allows future software that requires any of the modules to simply import them, rather than having to re-implement them. In line with good software quality practices, if any of the modules needs an upgrade, this upgrade simply has to be performed on the respective module, instead of upgrading every adjacent software that uses this module. This was the first goal of this thesis. After creating a solid foundation for robot software development, the focus shifted towards the creation of new functionality. The different tasks were obtained from the previous research of various robotic competitions. The idea is that if the robot can perform such tasks then it can participate in the competitions, while the same functionalities can be used around campus. The list aimed to be as long as possible with the goal of leaving the robot with as much functionality as possible while taking into consideration the time restraints of the development of this thesis. Seven tasks were selected. The implementation of each task is explained in detail. As each task was developed, the implemented steps were turned into modules, therefore respecting the initial goal of flexible and reusable software. Because of this, as more tasks were developed the following task’s implementation was increasingly simpler as some of the requirements were already available from the development of their predecessors. The tasks required knowledge from different areas of artificial intelligence. This lead to the broadening of my knowledge rather than specialization in a single area. With this work, we show how distinct robotic tasks were implemented. Due to the varied nature ofthe tasks, we show how to tackle a multitude of different problems that appear in the area of artificial intelligence. Additionally, the work presents an approach to create a solid foundation for the development and integration of increasingly more software. The tasks are benchmarked, meaning future updates ofthe tasks can be performed and proved superior through the comparison of their results.Os robôs de serviço são cada vez mais comuns devido aos avanços constantes na área da inteligência artificial, substituindo os humanos em tarefas cada vez mais complexas. Ao ter um robô autónomo e competente desempenhando tarefas diárias em vez do seu dono humano, a produtividade destes consequentemente aumenta. A pesquisa por melhores robôs de serviço levou à criação de competições robóticas onde tais tipos de robôs são avaliados e o estado da arte é forçado a avançar. Com o objetivo de possuir um robô de serviço que sirva as pessoas na universidade, bem como um dia participar em tais competições, o Socialab adquiriu um robô Turtlebot2. O laboratório tem proposto vários projetos ao longo dos anos, cada um adicionando novos níveis de funcionalidade ao robô. Com cada estudante que tem vindo a realizar o respetivo projeto, o software que foi desenvolvido tem estado continuamente a aumentar. Adicionalmente, cada projeto completado tem permanecido separado dos restantes e não tem sido utilizado desde o momento da sua criação. Por esta razão, o software desenvolvido está a ser desperdiçado. Portanto, é imperativo integrar todo o software disponível no robô. No entanto, como novos projetos serão desenvolvidos, este problema de projetos disjuntos poderá voltar a ocorrer após a integração dos projetos atuais. Ademais, com o aumento da funcionalidade que é desenvolvida, mais dificil e demorado será a sua integração. De forma a evitar este problema na sua totalidade, é necessário criar software estrutural que facilite o desenvolvimento de novas funcionalidades, bem como a sua integração com o software já existente. De forma a atingir este objetivo, foi desenvolvida uma classe cujo propósito é controlar a execução de todos os processos em execução no robô. Adicionalmente, foi efetuada uma pesquisa sobre diversas competições robóticas com o objetivo de identificar os tipos de funcionalidades mais comuns, que referimos como módulos. Depois foi realizada uma implementação de cada um destes módulos. Devido à universalidade destes módulos, a sua implementação permite que software futuro, que provavelmente depende de alguns dos módulos, apenas os tenha que importar, em contraste com ter que os re-implementar. Adicionalmente, emlinha comas práticas dequalidadede software, se cadaumdosmódulos precisa de uma atualização, esta apenas tem de ser realizada nos respetivos módulos, ao invés de ter de atualizar cada software adjacente que teve de o implementar. Este foi o primeiro objetivo da tese. Após a criação de uma fundação sólida para o desenvolvimento de software para robô, o foco transferiu-se para a criação de nova funcionalidade. Uma lista de tarefas robóticas foi obtida da pesquisa anterior sobre várias competições robóticas. A ideia é que se o robô é capaz de realizar tais tarefas então não só pode participar nas competições que as requerem, como também tem utilidade que pode ser utilizada pelas pessoas na universidade. A lista visava ser o mais extensa possível, tendo em conta as restrições temporais de desenvolvimento da tese, de modo a deixar o robô com o máximo de funcionalidades possível. Desta forma, sete tarefas foram escolhidas. A implementação de cada uma das tarefas é explicada em detalhe no seu capítulo respetivo. À medida que cada tarefa foi desenvolvida, os seus componentes individuais foram extraídos para novos módulos, passiveis de serem utilizados por outras funcionalidades, respeitando assim, o objetivo inicial de criar software flexível e reutilizável. Este fator tornou a criação das tarefas seguintes cada vez mais simples devido a estas dependerem de funcionalidades já implementadas nas anteriores. A implementação das tarefas exigiu conhecimento das diferentes áreas da inteligência artificial. Este facto levou à ampliação do meu conhecimento ao invés da especialização numa área, algo que é frequente na realização de teses. A realização deste trabalho demonstra como as distintas tarefas robóticas foram implementadas. Devido à natureza variada das tarefas, é demonstrado como enfrentar um conjunto diverso de problemas que podem aparecer na área da inteligência artificial. Adicionalmente, este trabalho apresenta uma abordagem para a criação de uma fundação sólida para o desenvolvimento e integração de novo software. Por último, as tarefas estão aferidas contra o estado da arte, significando que atualizações futuras às tarefas podem ser realizadas e provadas superiores através da comparação dos seus resultados

Constrained Collective Movement in Human-Robot Teams

This research focuses on improving human-robot co-navigation for teams of robots and humans navigating together as a unit while accomplishing a desired task. Frequently, the team’s co-navigation is strongly influenced by a predefined Standard Operating Procedure (SOP), which acts as a high-level guide for where agents should go and what they should do. In this work, I introduce the concept of Constrained Collective Movement (CCM) of a team to describe how members of the team perform inter-team and intra-team navigation to execute a joint task while balancing environmental and application-specific constraints. This work advances robots’ abilities to participate along side humans in applications such as urban search and rescue, firefighters searching for people in a burning building, and military teams performing a building clearing operation. Incorporating robots on such teams could reduce the number of human lives put in danger while increasing the team’s ability to conduct beneficial tasks such as carrying life saving equipment to stranded people. Most previous work on generating more complex collaborative navigation for human- robot teams focuses solely on using model-based methods. These methods usually suffer from the need for hard coding the rules to follow, which can require much time and domain knowledge and can lead to unnatural behavior. This dissertation investigates merging high-level model-based knowledge representation with low-level behavior cloning to achieve CCM of a human-robot team performing collaborative co-navigation. To evaluate the approach, experiments are performed in simulation with the detail-rich game design engine Unity. Experiments show that the designed approach can learn elements of high-level behaviors with accuracies up to 88%. Additionally, the approach is shown to learn low-level robot control behaviors with accuracies up to 89%. To the best of my knowledge, this is the first attempt to blend classical AI methods with state-of-the-art machine learning methods for human-robot team collaborative co-navigation. This not only allows for better human-robot team co-navigation, but also has implications for improving other teamwork based human-robot applications such as joint manufacturing and social assistive robotics

An intelligent multi-floor mobile robot transportation system in life science laboratories

In this dissertation, a new intelligent multi-floor transportation system based on mobile robot is presented to connect the distributed laboratories in multi-floor environment. In the system, new indoor mapping and localization are presented, hybrid path planning is proposed, and an automated doors management system is presented. In addition, a hybrid strategy with innovative floor estimation to handle the elevator operations is implemented. Finally the presented system controls the working processes of the related sub-system. The experiments prove the efficiency of the presented system

Cyber Physical System for Continuous Evaluation of Fall Risks to Enable Aging-In-Place

Every year, one out of three adults over the age of 65 falls, and about 30% of the falls result in moderate to severe injuries. The high rate of fall-related hospitalizations and the fact that falls are a major source of morbidity and mortality in older adults have motivated extensive interdisciplinary clinical and engineering research with a focus on fall prevention. This research is aimed at developing a medical Cyber Physical System (CPS) composed of a human supervised mobile robot and ambient intelligence sensors to provide continuous evaluation of environmental risks in the home. As a preventive measure to avoid falls, we propose use of mobile robots to detect possible fall risks inside a house. As a step-up to that, we also define a control framework for intelligent, networked mobile robots to semi-autonomously perform assistive and preventive tasks. This framework is integrated in a smart home that provides monitoring and control capabilities of environmental conditions such as objects blocking pathways or uneven surfaces. The main outcome of this work is the realization of this system at Worcester Polytechnic Institute\u27s (WPI) @Home testbed

Topological Mapping and Navigation in Real-World Environments

We introduce the Hierarchical Hybrid Spatial Semantic Hierarchy (H2SSH), a hybrid topological-metric map representation. The H2SSH provides a more scalable representation of both small and large structures in the world than existing topological map representations, providing natural descriptions of a hallway lined with offices as well as a cluster of buildings on a college campus. By considering the affordances in the environment, we identify a division of space into three distinct classes: path segments afford travel between places at their ends, decision points present a choice amongst incident path segments, and destinations typically exist at the start and end of routes. Constructing an H2SSH map of the environment requires understanding both its local and global structure. We present a place detection and classification algorithm to create a semantic map representation that parses the free space in the local environment into a set of discrete areas representing features like corridors, intersections, and offices. Using these areas, we introduce a new probabilistic topological simultaneous localization and mapping algorithm based on lazy evaluation to estimate a probability distribution over possible topological maps of the global environment. After construction, an H2SSH map provides the necessary representations for navigation through large-scale environments. The local semantic map provides a high-fidelity metric map suitable for motion planning in dynamic environments, while the global topological map is a graph-like map that allows for route planning using simple graph search algorithms. For navigation, we have integrated the H2SSH with Model Predictive Equilibrium Point Control (MPEPC) to provide safe and efficient motion planning for our robotic wheelchair, Vulcan. However, navigation in human environments entails more than safety and efficiency, as human behavior is further influenced by complex cultural and social norms. We show how social norms for moving along corridors and through intersections can be learned by observing how pedestrians around the robot behave. We then integrate these learned norms with MPEPC to create a socially-aware navigation algorithm, SA-MPEPC. Through real-world experiments, we show how SA-MPEPC improves not only Vulcan’s adherence to social norms, but the adherence of pedestrians interacting with Vulcan as well.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144014/1/collinej_1.pd

Railway Research

This book focuses on selected research problems of contemporary railways. The first chapter is devoted to the prediction of railways development in the nearest future. The second chapter discusses safety and security problems in general, precisely from the system point of view. In the third chapter, both the general approach and a particular case study of a critical incident with regard to railway safety are presented. In the fourth chapter, the question of railway infrastructure studies is presented, which is devoted to track superstructure. In the fifth chapter, the modern system for the technical condition monitoring of railway tracks is discussed. The compact on-board sensing device is presented. The last chapter focuses on modeling railway vehicle dynamics using numerical simulation, where the dynamical models are exploited

Skylab Saturn 1B flight manual

A Saturn 1B Flight Manual provides launch vehicle systems descriptions and predicted performance data for the Skylab missions. Vehicle SL-2 (SA-206) is the baseline for this manual; but, as a result of the great similarity, the material is representative of SL-3 and SL-4 launch vehicles, also. The Flight Manual is not a control document but is intended primarily as an aid to astronauts who are training for Skylab missions. In order to provide a comprehensive reference for that purpose, the manual also contains descriptions of the ground support interfaces, prelaunch operations, and emergency procedures. Mission variables and constraints are summarized, and mission control monitoring and data flow during launch preparation and flight are discussed