Challenges and solutions for autonomous ground robot scene understanding and navigation in unstructured outdoor environments: A review

The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, rough or smooth terrain, etc.) are required for autonomous robot navigation in unstructured outdoor environments. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges

A novel visual tracking scheme for unstructured indoor environments

In the ever-expanding sphere of assistive robotics, the pressing need for advanced methods capable of accurately tracking individuals within unstructured indoor settings has been magnified. This research endeavours to devise a realtime visual tracking mechanism that encapsulates high performance attributes while maintaining minimal computational requirements. Inspired by the neural processes of the human brain’s visual information handling, our innovative algorithm employs a pattern image, serving as an ephemeral memory, which facilitates the identification of motion within images. This tracking paradigm was subjected to rigorous testing on a Nao humanoid robot, demonstrating noteworthy outcomes in controlled laboratory conditions. The algorithm exhibited a remarkably low false detection rate, less than 4%, and target losses were recorded in merely 12% of instances, thus attesting to its successful operation. Moreover, the algorithm’s capacity to accurately estimate the direct distance to the target further substantiated its high efficacy. These compelling findings serve as a substantial contribution to assistive robotics. The proficient visual tracking methodology proposed herein holds the potential to markedly amplify the competencies of robots operating in dynamic, unstructured indoor settings, and set the foundation for a higher degree of complex interactive tasks

Path planning algorithms for autonomous navigation of a non-holonomic robot in unstructured environments

openPath planning is a crucial aspect of autonomous robot navigation, enabling robots to efficiently and safely navigate through complex environments. This thesis focuses on autonomous navigation for robots in dynamic and uncertain environments. In particular, the project aims to analyze the localization and path planning problems. A fundamental review of the existing literature on path planning algorithms has been carried on. Various factors affecting path planning, such as sensor data fusion, map representation, and motion constraints, are also analyzed. Thanks to the collaboration with E80 Group S.p.A., the project has been developed using ROS (Robot Operating System) on a Clearpath Dingo-O, an indoor mobile robot. To address the challenges posed by unstructured and dynamic environments, ROS follows a combined approach of using a global planner and a local planner. The global planner generates a high-level path, considering the overall environment, while the local planner handles real-time adjustments to avoid moving obstacles and optimize the trajectory. This thesis describes the role of the global planner in a ROS-framework. Performance benchmarking of traditional algorithms like Dijkstra and A*, as well as other techniques, is fundamental in order to understand the limits of these methods. In the end, the Hybrid A* algorithm is introduced as a promising approach for addressing the issues of unstructured environments for autonomous navigation of a non-holonomic robot. The core concepts and implementation details of the algorithm are discussed, emphasizing its ability to efficiently explore continuous state spaces and generate drivable paths.The effectiveness of the proposed path planning algorithms is evaluated through extensive simulations and real-world experiments using the mobile platform. Performance metrics such as path length, execution time, and collision avoidance are analyzed to assess the efficiency and reliability of the algorithms.Path planning is a crucial aspect of autonomous robot navigation, enabling robots to efficiently and safely navigate through complex environments. This thesis focuses on autonomous navigation for robots in dynamic and uncertain environments. In particular, the project aims to analyze the localization and path planning problems. A fundamental review of the existing literature on path planning algorithms has been carried on. Various factors affecting path planning, such as sensor data fusion, map representation, and motion constraints, are also analyzed. Thanks to the collaboration with E80 Group S.p.A., the project has been developed using ROS (Robot Operating System) on a Clearpath Dingo-O, an indoor mobile robot. To address the challenges posed by unstructured and dynamic environments, ROS follows a combined approach of using a global planner and a local planner. The global planner generates a high-level path, considering the overall environment, while the local planner handles real-time adjustments to avoid moving obstacles and optimize the trajectory. This thesis describes the role of the global planner in a ROS-framework. Performance benchmarking of traditional algorithms like Dijkstra and A*, as well as other techniques, is fundamental in order to understand the limits of these methods. In the end, the Hybrid A* algorithm is introduced as a promising approach for addressing the issues of unstructured environments for autonomous navigation of a non-holonomic robot. The core concepts and implementation details of the algorithm are discussed, emphasizing its ability to efficiently explore continuous state spaces and generate drivable paths.The effectiveness of the proposed path planning algorithms is evaluated through extensive simulations and real-world experiments using the mobile platform. Performance metrics such as path length, execution time, and collision avoidance are analyzed to assess the efficiency and reliability of the algorithms

Towards Robust UAV Tracking in GNSS-Denied Environments: A Multi-LiDAR Multi-UAV Dataset

With the increasing prevalence of drones in various industries, the navigation and tracking of unmanned aerial vehicles (UAVs) in challenging environments, particularly GNSS-denied areas, have become crucial concerns. To address this need, we present a novel multi-LiDAR dataset specifically designed for UAV tracking. Our dataset includes data from a spinning LiDAR, two solid-state LiDARs with different Field of View (FoV) and scan patterns, and an RGB-D camera. This diverse sensor suite allows for research on new challenges in the field, including limited FoV adaptability and multi-modality data processing. The dataset facilitates the evaluation of existing algorithms and the development of new ones, paving the way for advances in UAV tracking techniques. Notably, we provide data in both indoor and outdoor environments. We also consider variable UAV sizes, from micro-aerial vehicles to more standard commercial UAV platforms. The outdoor trajectories are selected with close proximity to buildings, targeting research in UAV detection in urban areas, e.g., within counter-UAV systems or docking for UAV logistics. In addition to the dataset, we provide a baseline comparison with recent LiDAR-based UAV tracking algorithms, benchmarking the performance with different sensors, UAVs, and algorithms. Importantly, our dataset shows that current methods have shortcomings and are unable to track UAVs consistently across different scenarios

Scene understanding for autonomous robots operating in indoor environments

Mención Internacional en el título de doctorThe idea of having robots among us is not new. Great efforts are continually made to replicate human intelligence, with the vision of having robots performing different activities, including hazardous, repetitive, and tedious tasks. Research has demonstrated that robots are good at many tasks that are hard for us, mainly in terms of precision, efficiency, and speed. However, there are some tasks that humans do without much effort that are challenging for robots. Especially robots in domestic environments are far from satisfactorily fulfilling some tasks, mainly because these environments are unstructured, cluttered, and with a variety of environmental conditions to control. This thesis addresses the problem of scene understanding in the context of autonomous robots operating in everyday human environments. Furthermore, this thesis is developed under the HEROITEA research project that aims to develop a robot system to help elderly people in domestic environments as an assistant. Our main objective is to develop different methods that allow robots to acquire more information from the environment to progressively build knowledge that allows them to improve the performance on high-level robotic tasks. In this way, scene understanding is a broad research topic, and it is considered a complex task due to the multiple sub-tasks that are involved. In that context, in this thesis, we focus on three sub-tasks: object detection, scene recognition, and semantic segmentation of the environment. Firstly, we implement methods to recognize objects considering real indoor environments. We applied machine learning techniques incorporating uncertainties and more modern techniques based on deep learning. Besides, apart from detecting objects, it is essential to comprehend the scene where they can occur. For this reason, we propose an approach for scene recognition that considers the influence of the detected objects in the prediction process. We demonstrate that the exiting objects and their relationships can improve the inference about the scene class. We also consider that a scene recognition model can benefit from the advantages of other models. We propose a multi-classifier model for scene recognition based on weighted voting schemes. The experiments carried out in real-world indoor environments demonstrate that the adequate combination of independent classifiers allows obtaining a more robust and precise model for scene recognition. Moreover, to increase the understanding of a robot about its surroundings, we propose a new division of the environment based on regions to build a useful representation of the environment. Object and scene information is integrated into a probabilistic fashion generating a semantic map of the environment containing meaningful regions within each room. The proposed system has been assessed on simulated and real-world domestic scenarios, demonstrating its ability to generate consistent environment representations. Lastly, full knowledge of the environment can enhance more complex robotic tasks; that is why in this thesis, we try to study how a complete knowledge of the environment influences the robot’s performance in high-level tasks. To do so, we select an essential task, which is searching for objects. This mundane task can be considered a precondition to perform many complex robotic tasks such as fetching and carrying, manipulation, user requirements, among others. The execution of these activities by service robots needs full knowledge of the environment to perform each task efficiently. In this thesis, we propose two searching strategies that consider prior information, semantic representation of the environment, and the relationships between known objects and the type of scene. All our developments are evaluated in simulated and real-world environments, integrated with other systems, and operating in real platforms, demonstrating their feasibility to implement in real scenarios, and in some cases outperforming other approaches. We also demonstrate how our representation of the environment can boost the performance of more complex robotic tasks compared to more standard environmental representations.La idea de tener robots entre nosotros no es nueva. Continuamente se realizan grandes esfuerzos para replicar la inteligencia humana, con la visión de tener robots que realicen diferentes actividades, incluidas tareas peligrosas, repetitivas y tediosas. La investigación ha demostrado que los robots son buenos en muchas tareas que resultan difíciles para nosotros, principalmente en términos de precisión, eficiencia y velocidad. Sin embargo, existen tareas que los humanos realizamos sin mucho esfuerzo y que son un desafío para los robots. Especialmente, los robots en entornos domésticos están lejos de cumplir satisfactoriamente algunas tareas, principalmente porque estos entornos no son estructurados, pueden estar desordenados y cuentan con una gran variedad de condiciones ambientales que controlar. Esta tesis aborda el problema de la comprensión de la escena en el contexto de robots autónomos que operan en entornos humanos cotidianos. Asimismo, esta tesis se desarrolla en el marco del proyecto de investigación HEROITEA que tiene como objetivo desarrollar un sistema robótico que funcione como asistente para ayudar a personas mayores en entornos domésticos. Nuestro principal objetivo es desarrollar diferentes métodos que permitan a los robots adquirir más información del entorno a fin de construir progresivamente un conocimiento que les permita mejorar su desempeño en tareas robóticas más complejas. En este sentido, la comprensión de escenas es un tema de investigación amplio, y se considera una tarea compleja debido a las múltiples subtareas involucradas. En esta tesis nos enfocamos específicamente en tres subtareas: detección de objetos, reconocimiento de escenas y etiquetado semántico del entorno. Por un lado, implementamos métodos para el reconocimiento de objectos considerando entornos interiores reales. Aplicamos técnicas de aprendizaje automático incorporando incertidumbres y técnicas más modernas basadas en aprendizaje profundo. Además, aparte de detectar objetos, es fundamental comprender la escena donde estos se encuentran. Por esta razón, proponemos un modelo para el reconocimiento de escenas que considera la influencia de los objetos detectados en el proceso de predicción. Demostramos que los objetos existentes y sus relaciones pueden mejorar el proceso de inferencia de la categoría de la escena. También consideramos que un modelo de reconocimiento de escenas puede beneficiarse de las ventajas de otros modelos. Por ello, proponemos un multiclasificador para el reconocimiento de escenas basado en esquemas de votación ponderados. Los experimentos llevados a cabo en entornos interiores reales demuestran que la combinación adecuada de clasificadores independientes permite obtener un modelo más robusto y preciso para el reconocimiento de escenas. Adicionalmente, para aumentar la comprensión de un robot acerca de su entorno, proponemos una nueva división del entorno basada en regiones a fin de construir una representación útil del entorno. La información de objetos y de la escena se integra de forma probabilística generando un mapa semántico que contiene regiones significativas dentro de cada habitación. El sistema propuesto ha sido evaluado en entornos domésticos simulados y reales, demostrando su capacidad para generar representaciones consistentes del entorno. Por otro lado, el conocimiento integral del entorno puede mejorar tareas robóticas más complejas; es por ello que en esta tesis analizamos cómo el conocimiento completo del entorno influye en el desempeño del robot en tareas de alto nivel. Para ello, seleccionamos una tarea fundamental, que es la búsqueda de objetos. Esta tarea mundana puede considerarse una condición previa para realizar diversas tareas robóticas complejas, como transportar objetos, tareas de manipulación, atender requerimientos del usuario, entre otras. La ejecución de estas actividades por parte de robots de servicio requiere un conocimiento profundo del entorno para realizar cada tarea de manera eficiente. En esta tesis proponemos dos estrategias de búsqueda de objetos que consideran información previa, la representación semántica del entorno, las relaciones entre los objetos conocidos y el tipo de escena. Todos nuestros desarrollos son evaluados en entornos simulados y reales, integrados con otros sistemas y operando en plataformas reales, demostrando su viabilidad de ser implementados en escenarios reales y, en algunos casos, superando a otros enfoques. También demostramos cómo nuestra representación del entorno puede mejorar el desempeño de tareas robóticas más complejas en comparación con representaciones del entorno más tradicionales.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Carlos Balaguer Bernaldo de Quirós.- Secretario: Fernando Matía Espada.- Vocal: Klaus Strob

Multimodal machine learning for intelligent mobility

Scientific problems are solved by finding the optimal solution for a specific task. Some problems can be solved analytically while other problems are solved using data driven methods. The use of digital technologies to improve the transportation of people and goods, which is referred to as intelligent mobility, is one of the principal beneficiaries of data driven solutions. Autonomous vehicles are at the heart of the developments that propel Intelligent Mobility. Due to the high dimensionality and complexities involved in real-world environments, it needs to become commonplace for intelligent mobility to use data-driven solutions. As it is near impossible to program decision making logic for every eventuality manually. While recent developments of data-driven solutions such as deep learning facilitate machines to learn effectively from large datasets, the application of techniques within safety-critical systems such as driverless cars remain scarce.Autonomous vehicles need to be able to make context-driven decisions autonomously in different environments in which they operate. The recent literature on driverless vehicle research is heavily focused only on road or highway environments but have discounted pedestrianized areas and indoor environments. These unstructured environments tend to have more clutter and change rapidly over time. Therefore, for intelligent mobility to make a significant impact on human life, it is vital to extend the application beyond the structured environments. To further advance intelligent mobility, researchers need to take cues from multiple sensor streams, and multiple machine learning algorithms so that decisions can be robust and reliable. Only then will machines indeed be able to operate in unstructured and dynamic environments safely. Towards addressing these limitations, this thesis investigates data driven solutions towards crucial building blocks in intelligent mobility. Specifically, the thesis investigates multimodal sensor data fusion, machine learning, multimodal deep representation learning and its application of intelligent mobility. This work demonstrates that mobile robots can use multimodal machine learning to derive driver policy and therefore make autonomous decisions.To facilitate autonomous decisions necessary to derive safe driving algorithms, we present an algorithm for free space detection and human activity recognition. Driving these decision-making algorithms are specific datasets collected throughout this study. They include the Loughborough London Autonomous Vehicle dataset, and the Loughborough London Human Activity Recognition dataset. The datasets were collected using an autonomous platform design and developed in house as part of this research activity. The proposed framework for Free-Space Detection is based on an active learning paradigm that leverages the relative uncertainty of multimodal sensor data streams (ultrasound and camera). It utilizes an online learning methodology to continuously update the learnt model whenever the vehicle experiences new environments. The proposed Free Space Detection algorithm enables an autonomous vehicle to self-learn, evolve and adapt to new environments never encountered before. The results illustrate that online learning mechanism is superior to one-off training of deep neural networks that require large datasets to generalize to unfamiliar surroundings. The thesis takes the view that human should be at the centre of any technological development related to artificial intelligence. It is imperative within the spectrum of intelligent mobility where an autonomous vehicle should be aware of what humans are doing in its vicinity. Towards improving the robustness of human activity recognition, this thesis proposes a novel algorithm that classifies point-cloud data originated from Light Detection and Ranging sensors. The proposed algorithm leverages multimodality by using the camera data to identify humans and segment the region of interest in point cloud data. The corresponding 3-dimensional data was converted to a Fisher Vector Representation before being classified by a deep Convolutional Neural Network. The proposed algorithm classifies the indoor activities performed by a human subject with an average precision of 90.3%. When compared to an alternative point cloud classifier, PointNet[1], [2], the proposed framework out preformed on all classes. The developed autonomous testbed for data collection and algorithm validation, as well as the multimodal data-driven solutions for driverless cars, is the major contributions of this thesis. It is anticipated that these results and the testbed will have significant implications on the future of intelligent mobility by amplifying the developments of intelligent driverless vehicles.</div

Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems

Smart buildings are increasingly using Internet of Things (IoT)-based wireless sensing systems to reduce their energy consumption and environmental impact. As a result of their compact size and ability to sense, measure, and compute all electrical properties, Internet of Things devices have become increasingly important in our society. A major contribution of this study is the development of a comprehensive IoT-based framework for smart city energy management, incorporating multiple components of IoT architecture and framework. An IoT framework for intelligent energy management applications that employ intelligent analysis is an essential system component that collects and stores information. Additionally, it serves as a platform for the development of applications by other companies. Furthermore, we have studied intelligent energy management solutions based on intelligent mechanisms. The depletion of energy resources and the increase in energy demand have led to an increase in energy consumption and building maintenance. The data collected is used to monitor, control, and enhance the efficiency of the system

Contributions to Intelligent Scene Understanding of Unstructured Environments from 3D lidar sensors

Además, la viabilidad de este enfoque es evaluado mediante la implementación de cuatro tipos de clasificadores de aprendizaje supervisado encontrados en métodos de procesamiento de escenas: red neuronal, máquina de vectores de soporte, procesos gaussianos, y modelos de mezcla gaussiana. La segmentación de objetos es un paso más allá hacia el entendimiento de escena, donde conjuntos de puntos 3D correspondientes al suelo y otros objetos de la escena son aislados. La tesis propone nuevas contribuciones a la segmentación de nubes de puntos basados en mapas de vóxeles caracterizados geométricamente. En concreto, la metodología propuesta se compone de dos pasos: primero, una segmentación del suelo especialmente diseñado para entornos naturales; y segundo, el posterior aislamiento de objetos individuales. Además, el método de segmentación del suelo es integrado en una nueva técnica de mapa de navegabilidad basado en cuadrícula de ocupación el cuál puede ser apropiado para robots móviles en entornos naturales. El diseño y desarrollo de un nuevo y asequible sensor lidar 3D de alta resolución también se ha propuesto en la tesis. Los nuevos MBLs, tales como los desarrollados por Velodyne, están siendo cada vez más un tipo de sensor 3D asequible y popular que ofrece alto ratio de datos en un campo de visión vertical (FOV) limitado. El diseño propuesto consiste en una plataforma giratoria que mejora la resolución y el FOV vertical de un Velodyne VLP-16 de 16 haces. Además, los complejos patrones de escaneo producidos por configuraciones de MBL que rotan se analizan tanto en simulaciones de esfera hueca como en escáneres reales en entornos representativos. Fecha de Lectura de Tesis: 11 de julio 2018.Ingeniería de Sistemas y Automática Resumen tesis: Los sensores lidar 3D son una tecnología clave para navegación, localización, mapeo y entendimiento de escenas en vehículos no tripulados y robots móviles. Esta tecnología, que provee nubes de puntos densas, puede ser especialmente adecuada para nuevas aplicaciones en entornos naturales o desestructurados, tales como búsqueda y rescate, exploración planetaria, agricultura, o exploración fuera de carretera. Esto es un desafío como área de investigación que incluye disciplinas que van desde el diseño de sensor a la inteligencia artificial o el aprendizaje automático (machine learning). En este contexto, esta tesis propone contribuciones al entendimiento inteligente de escenas en entornos desestructurados basado en medidas 3D de distancia a nivel del suelo. En concreto, las contribuciones principales incluyen nuevas metodologías para la clasificación de características espaciales, segmentación de objetos, y evaluación de navegabilidad en entornos naturales y urbanos, y también el diseño y desarrollo de un nuevo lidar rotatorio multi-haz (MBL). La clasificación de características espaciales es muy relevante porque es extensamente requerida como un paso fundamental previo a los problemas de entendimiento de alto nivel de una escena. Las contribuciones de la tesis en este respecto tratan de mejorar la eficacia, tanto en carga computacional como en precisión, de clasificación de aprendizaje supervisado de características de forma espacial (forma tubular, plana o difusa) obtenida mediante el análisis de componentes principales (PCA). Esto se ha conseguido proponiendo un concepto eficiente de vecindario basado en vóxel en una contribución original que define los procedimientos de aprendizaje “offline” y clasificación “online” a la vez que cinco definiciones alternativas de vectores de características basados en PCA