Autonomous Hybrid Ground/Aerial Mobility in Unknown Environments

Hybrid ground and aerial vehicles can possess distinct advantages over ground-only or flight-only designs in terms of energy savings and increased mobility. In this work we outline our unified framework for controls, planning, and autonomy of hybrid ground/air vehicles. Our contribution is three-fold: 1) We develop a control scheme for the control of passive two-wheeled hybrid ground/aerial vehicles. 2) We present a unified planner for both rolling and flying by leveraging differential flatness mappings. 3) We conduct experiments leveraging mapping and global planning for hybrid mobility in unknown environments, showing that hybrid mobility uses up to five times less energy than flying only

Metadata of the chapter that will be visualized in SpringerLink Book Title Combinatorial Optimization and Applications Series Title Chapter Title Optimal Strategy for Walking in Streets with Minimum Number of Turns for a Simple Robot Optimal Strategy for

Abstract We consider the problem of walking a simple robot in an unknown street. The robot that cannot infer any geometric properties of the street traverses the environment to reach a target , starting from a point . The robot has a minimal sensing capability that can only report the discontinuities in the depth information (gaps), and location of the target point once it enters in its visibility region. Also, the robot can only move towards the gaps while moving along straight lines is cheap, but rotation is expensive for the robot. We maintain the location of some gaps in a tree data structure of constant size. The tree is dynamically updated during the movement. Using the data structure, we present an online strategy that generates a search path for the robot with optimal number of turns. Keywords (separated by '-') Computational geometry -Minimum link path -Simple robot -Street polygon -Unknown environment Abstract. We consider the problem of walking a simple robot in an unknown street. The robot that cannot infer any geometric properties of the street traverses the environment to reach a target t, starting from a point s. The robot has a minimal sensing capability that can only report the discontinuities in the depth information (gaps), and location of the target point once it enters in its visibility region. Also, the robot can only move towards the gaps while moving along straight lines is cheap, but rotation is expensive for the robot. We maintain the location of some gaps in a tree data structure of constant size. The tree is dynamically updated during the movement. Using the data structure, we present an online strategy that generates a search path for the robot with optimal number of turns

Equivalent Environments and Covering Spaces for Robots

This paper formally defines a robot system, including its sensing and actuation components, as a general, topological dynamical system. The focus is on determining general conditions under which various environments in which the robot can be placed are indistinguishable. A key result is that, under very general conditions, covering maps witness such indistinguishability. This formalizes the intuition behind the well studied loop closure problem in robotics. An important special case is where the sensor mapping reports an invariant of the local topological (metric) structure of an environment because such structure is preserved by (metric) covering maps. Whereas coverings provide a sufficient condition for the equivalence of environments, we also give a necessary condition using bisimulation. The overall framework is applied to unify previously identified phenomena in robotics and related fields, in which moving agents with sensors must make inferences about their environments based on limited data. Many open problems are identified.Comment: 34 pages, 8 figure

Mapping and Pursuit-Evasion Strategies For a Simple Wall-Following Robot

This paper defines and analyzes a simple robot with local sensors that moves in an unknown polygonal environment. The robot can execute wall-following motions and can traverse the interior of the environment only when following parallel to an edge. The robot has no global sensors that would allow precise mapping or localization. Special information spaces are introduced for this particular model. Using these, strategies are presented for solving several tasks: 1) counting vertices, 2) computing the path winding number, 3) learning a combinatorial map, called the cut ordering, that encodes partial geometric information, and 4) solving pursuit-evasion problems.DARPA/HR0011-05-1-0008NSF/0535007ONR/N000014-02-1-0488published or submitted for publicationnot peer reviewe

Avaliação de algoritmos de exploração de ambientes por robôs móveis

Trabalho de conclusão de curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Curso de Graduação em Engenharia de Controle e Automação, 2018.Atividades como exploração e cobertura auxiliam robôs a descobrir o ambiente, maximizando a região coberta tanto fisicamente quanto sensorialmente. O foco desse trabalho é a avaliação de diferentes técnicas exploratórias com robôs móveis sem construção de mapa, tais técnicas são baseadas em direcionamento probabilístico. Para isso é utilizado um modelo de robô móvel Pioneer 3-DX, presente no LARA (Laboratório de Automação e Robótica), dotado de sensor visual Microsoft Kinect e utilizando o ambiente de desenvolvimento aberto ROS (Robot Operating System)The exploration and coverage problem help mobile robots to know their environment and maximize the total area which is covered by the body of robot or by their sensors. This work presents the evaluation of different exploration techniques of mobile robots without any map construction or representation, and these techniques are based in stochastic goals. For this purpose, is used a Pioneer 3-DX robot model, from the Automation and Robotics Laboratory (LARA), and is equipped with a Microsoft Kinect visual sensor and works with Robot Operating System (ROS) framework

Auto-localização e mapeamento de ambientes : uma abordagem para robôs simples

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade UnB Gama, 2017.Grande parte das pesquisas relacionadas à robótica é focada, principalmente, na mobilidade do robô. Isto ocorre pela necessidade, na maioria das atividades, da navegação e auto-localização no ambiente. Com este objetivo, a técnica de SLAM (Auto- localização e mapeamento simultâneos de ambientes) vem sendo implementada em diversos contextos por toda a comunidade de robótica. Esta pesquisa buscou analisar técnicas renomadas de auto-localização no contexto da robótica mundial, a partir da execução de uma revisão sistemática sobre o tema, selecionando a técnica do Filtro de Partículas para adaptação e implementação no contexto limitado da Robótica Educacional. Durante as etapas de implementação e análise dos resultados, a pesquisa busca documentar de maneira clara e objetiva os procedimentos realizados, garantindo a possibilidade da execução dos procedimentos por interessados no assunto. Além da aplicação no contexto educacional, deve-se ressaltar que esta pesquisa faz referência a utilização de robôs simples no processo de auto-localização, o que abrange sua utilização também em contextos reais, porém com limitações de hardware.This research sought to analyse renowned techniques of auto localization in the context of the current world of robotics, starting from the execution of a sistematic revision about the theme, selecting the Particle Filter to the adaptation and implementation in the limited context of Educational Robotics. During the steps of the analysis of results, the research sought to document clearly and objectively the proceedings, ensuring the possibility of the execution of them by the interested researchers in the theme. Besides the application on the educational context, it must be emphasized that this search makes reference to the utilization of simple robots on the auto localization process, which also includes its utilization on real contexts, with hardware limitations, however

Hazardous Chemical Source Localisation in Indoor Environments Using Plume-tracing Methods

Bio-inspired chemical plume-tracing methods have been applied to mobile robots to detect chemical emissions in the form of plumes and localise the plume sources in various indoor environments. Nevertheless, it has been found from the literature that most of the research has focused on plume tracing in free-stream plumes, such as indoor plumes where the chemical sources are located away from walls. Moreover, most of the experimental and numerical studies regarding the assessment of indoor plume-tracing algorithms have been undertaken in laboratory-scale environments. Since fluid fields and chemical concentration distributions of plumes near walls can be different from those of free-stream plumes, understanding of the performance of existing plume-tracing algorithms in near-wall regions is needed. In addition, the performance of different plume-tracing algorithms in detecting and tracing wall plumes in large-scale indoor environments is still unclear. In this research, a simulation framework combining ANSYS/FLUENT, which is used for simulating fluid fields and chemical concentration distributions of the environment, and MATLAB, with which plume-tracing algorithms are coded, is applied. In general, a plume-tracing algorithm can be divided into three stages: plume sensing, plume tracking and source localisation for analysis and discussion. In the first part of this research, an assessment of the performance of sixteen widely-used plume-tracing algorithms equipped with a concentration-distance obstacle avoidance method, was undertaken in two different scenarios. In one scenario, a single chemical source is located away from the walls in a wind-tunnel-like channel and in the other scenario, the chemical source is located near a wall. It is found that normal casting, surge anemotaxis and constant stepsize together performed the best, when compared with all the other algorithms. Also, the performance of the concentration-distance obstacle avoidance method is unsatisfactory. By applying an along-wall obstacle avoidance method, an algorithm called vallumtaxis, has been proposed and proved to contribute to higher efficiencies for plume tracing especially when searching in wall plumes. The results and discussion of the first part are presented in Chapter 4 of this thesis. In the second part, ten plume-tracing algorithms were tested and compared in four scenarios in a large-scale indoor environment: an underground warehouse. In these four scenarios, the sources are all on walls while their locations are different. The preliminary testing results of five algorithms show that for most failure cases, the robot failed at source localisation stage. Consequently, with different searching strategies at source localisation stage, this research investigated five further algorithms. The results demonstrated that the algorithm with a specially-designed pseudo casting source localisation method is the best approach to localising hazardous plume sources in the underground warehouse given in this research or other similar environments, among all the tested algorithms. The second part of the study is reported in Chapter 5 of this thesis.Thesis (MPhil) -- University of Adelaide, School of Mechanical Engineering, 202

Large-scale Multi-agent Decision-making Using Mean Field Game Theory and Reinforcement Learning

The Multi-agent system (MAS) optimal control problem is a recently emerging research topic that benefits industries such as robotics, communication, and power systems. The traditional MAS control algorithms are developed by extending the single agent optimal controllers, requiring heavy information exchange. Moreover, the information exchanged within the MAS needs to be used to compute the optimal control resulting in the coupling between the computational complexity and the agent number. With the increasing need for large-scale MAS in practical applications, the existing MAS optimal control algorithms suffer from the ``curse of dimensionality" problem and limited communication resources. Therefore, a new type of MAS optimal control framework that features a decentralized and computational friendly decision process is desperately needed. To deal with the aforementioned problems, the mean field game theory is introduced to generate a decentralized optimal control framework named the Actor-critic-mass (ACM). Moreover, the ACM algorithm is improved by eliminating constraints such as homogeneous agents and cost functions. Finally, the ACM algorithm is utilized in two applications