A Comprehensive Review on Autonomous Navigation

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed

Mechatronic Systems

Mechatronics, the synergistic blend of mechanics, electronics, and computer science, has evolved over the past twenty five years, leading to a novel stage of engineering design. By integrating the best design practices with the most advanced technologies, mechatronics aims at realizing high-quality products, guaranteeing at the same time a substantial reduction of time and costs of manufacturing. Mechatronic systems are manifold and range from machine components, motion generators, and power producing machines to more complex devices, such as robotic systems and transportation vehicles. With its twenty chapters, which collect contributions from many researchers worldwide, this book provides an excellent survey of recent work in the field of mechatronics with applications in various fields, like robotics, medical and assistive technology, human-machine interaction, unmanned vehicles, manufacturing, and education. We would like to thank all the authors who have invested a great deal of time to write such interesting chapters, which we are sure will be valuable to the readers. Chapters 1 to 6 deal with applications of mechatronics for the development of robotic systems. Medical and assistive technologies and human-machine interaction systems are the topic of chapters 7 to 13.Chapters 14 and 15 concern mechatronic systems for autonomous vehicles. Chapters 16-19 deal with mechatronics in manufacturing contexts. Chapter 20 concludes the book, describing a method for the installation of mechatronics education in schools

Unmanned Ground Vehicle navigation and coverage hole patching in Wireless Sensor Networks

This dissertation presents a study of an Unmanned Ground Vehicle (UGV) navigation and coverage hole patching in coordinate-free and localization-free Wireless Sensor Networks (WSNs). Navigation and coverage maintenance are related problems since coverage hole patching requires effective navigation in the sensor network environment. A coordinate-free and localization-free WSN that is deployed in an ad-hoc fashion and does not assume the availability of GPS information is considered. The system considered is decentralized and can be self-organized in an event-driven manner where no central controller or global map is required. A single-UGV, single-destination navigation problem is addressed first. The UGV is equipped with a set of wireless listeners that determine the slope of a navigation potential field generated by the wireless sensor and actuator network. The navigation algorithm consists of sensor node level-number assignment that is determined based on a hop-distance from the network destination node and UGV navigation through the potential field created by triplets of actuators in the network. A multi-UGV, multi-destination navigation problem requires a path-planning and task allocation process. UGVs inform the network about their proposed destinations, and the network provides feedback if conflicts are found. Sensor nodes store, share, and communicate to UGVs in order to allocate the navigation tasks. A special case of a single-UGV, multi-destination navigation problem that is equivalent to the well-known Traveling Salesman Problem is discussed. The coverage hole patching process starts after a UGV reaches the hole boundary. For each hole boundary edge, a new node is added along its perpendicular bisector, and the entire hole is patched by adding nodes around the hole boundary edges. The communication complexity and present simulation examples and experimental results are analyzed. Then, a Java-based simulation testbed that is capable of simulating both the centralized and distributed sensor and actuator network algorithms is developed. The laboratory experiment demonstrates the navigation algorithm (single-UGV, single-destination) using Cricket wireless sensors and an actuator network and Pioneer 3-DX robot

Proceedings of the 4th field robot event 2006, Stuttgart/Hohenheim, Germany, 23-24th June 2006

Zeer uitgebreid verslag van het 4e Fieldrobotevent, dat gehouden werd op 23 en 24 juni 2006 in Stuttgart/Hohenhei

Methods and Devices for Mobile Robot Navigation and Mapping in Unstructured Environments

2006/2007The work described in this thesis has been carried out in the context of the exploration of an unknown environment by an autonomous mobile robot. It is rather difficult to imagine a robot that is truly autonomous without being capable of acquiring a model of its environment. This model can be built by the robot exploring the environment and registering the data collected with the sensors over time. In the last decades a lot of progress has been made regarding techniques focused on environments which posses a lot of structure. This thesis contributes to the goal of extending existing techniques to unstructured environments by proposing new methods and devices for mapping in real-time. The first part of the thesis addresses some of the problems of ultrasonic sensors which are widely used in mobile robotics for mapping and obstacle detection during exploration. Ultrasonic sensors have two main shortcomings leading to disappointing performance: uncertainty in target location and multiple reflections. The former is caused by wide beam width and the latter gives erroneous distance measurements because of the insertion of spikes not directly connected to the target. With the aim of registering a detailed contour of the environment surrounding the robot, a sensing device was developed by focusing the ultrasonic beam of the most common ultrasonic sensor to extend its range and improve the spatial resolution. Extended range makes this sensor much more suitable for mapping of outdoor environments which are typically larger. Improved spatial resolution enables the usage of recent laser scan matching techniques on the sonar scans of the environment collected with the sensor. Furthermore, an algorithm is proposed to mitigate some undesirable effects and problems of the ultrasonic sensor. The method registers the acquired raw ultrasonic signal in order to obtain a reliable mapping of the environment. A single sonar measurement consists of a number of pulses reflected by an obstacle. From a series of sensor readings at different sonar angles the sequence of pulses reflected by the environment changes according to the distance between the sensor and the environment. This results in an image of sonar reflections that can be built by representing the reading angle on the horizontal axis and the echoes acquired by the sensor on the vertical one. The characteristics of a sonar emission result in a texture embedded in the image. The algorithm performs a 2D texture analysis of the sonar reflections image in such a way that the texture continuity is analyzed at the overall image scale, thus enabling the correction of the texture continuity by restoring weak or missing reflections. The first part of the algorithm extracts geometric semantic attributes from the image in order to enhance and correct the signal. The second part of the algorithm applies heuristic rules to find the leading pulse of the echo and to estimate the obstacle location in points where otherwise it would not be possible due to noise or lack of signal. The method overcomes inherent problems of ultrasonic sensing in case of high irregularities and missing reflections. It is suitable for map building during mobile robot exploration missions. It's main limitation is small coverage area. This area however increases during exploration as more scans are processed from different positions. Localization and mapping problems were addressed in the second part of the thesis. The main issue in robot self-localization is how to match sensed data, acquired with devices such as laser range finders or ultrasonic range sensors, against reference map information. In particular scan matching techniques are used to correct the accumulated positional error using dead reckoning sensors like odometry and inertial sensors and thus cancel out the effects of noise on localization and mapping. Given the reference scan from a known position and the new scan in unknown or approximately known position, the scan matching algorithm should provide a position estimate which is close to the true robot position from which the new scan was acquired. A genetic based optimization algorithm that solves this problem called GLASM is proposed. It uses a novel fitness function which is based on a look up table requiring little memory to speed the search. Instead of searching for corresponding point pairs and then computing the mean of the distances between them, as in other algorithms, the fitness is directly evaluated by matching points which, after the projection on the same coordinate frame, fall in the search window around the previous scan. It has a linear computational complexity, whereas the algorithms based on correspondences have a quadratic cost. The GLASM algorithm has been compared to it's closest rivals. The results of comparison are reported in the thesis and show, to summarize, that GLASM outperforms them both in speed and in matching success ratio. Glasm is suitable for implementation in feature-poor environments and robust to high sensor noise, as is the case with the sonar readings used in this thesis which are much noisier than laser scanners. The algorithm does not place a high computational burden on the processor, which is important for real world applications where the power consumption is a concern, and scales easily on multiprocessor systems. The algorithm does not require an initial position estimate and is suitable for unstructured environments. In mobile robotics it is critical to evaluate the above mentioned methods and devices in real world applications on systems with limited power and computational resources. In the third part of the thesis some new theoretical results are derived concerning open problems in non-preemptive scheduling of periodic tasks on a uniprocessor. This results are then used to propose a design methodology which is used in an application on a mobile robot. The mobile robot is equipped with an embedded system running a new real-time kernel called Yartek with a non-preemptive scheduler of periodic tasks. The application is described and some preliminary mapping results are presented. The real-time operating system has been developed in a collaborative work for an embedded platform based on a Coldfire microcontroller. The operating system allows the creation and running of tasks and offers a dynamic management of a contiguous memory using a first-fit criterion. The tasks can be real-time periodic scheduled with non-preemptive EDF, or non real-time. In order to improve the usability of the system, a RAM-disk is included: it is actually an array defined in the main memory and managed using pointers, therefore its operation is very fast. The goal was to realize small autonomous embedded system for implementing real-time algorithms for non visual robotic sensors, such as infrared, tactile, inertial devices or ultrasonic proximity sensors. The system provides the processing requested by non visual sensors without imposing a computation burden on the main processor of the robot. In particular, the embedded system described in this thesis provides the robot with the environmental map acquired with the ultrasonic sensors. Yartek has low footprint and low overhead. In order to compare Yartek with another operating system a porting of RTAI for Linux has been performed on the Avnet M5282EVB board and testing procedures were implemented. Tests regarding context switch time, jitter time and interrupt latency time are reported to describe the performance of Yartek. The contributions of this thesis include the presentation of new algorithms and devices, their applications and also some theoretical results. They are briefly summarized as: A focused ultrasonic sensing device is developed and used in mapping applications. An algorithm that processes the ultrasonic readings in order to develop a reliable map of the environment is presented. A new genetic algorithm for scan matching called GLASM is proposed. Schedulability conditions for non-preemptive scheduling in a hard real-time operating system are introduced and a design methodology is proposed. A real-time kernel for embedded systems in mobile robotics is presented. A practical robotic application is described and implementation details and trade-offs are explained.XIX Ciclo197

Real-Time Mapping Using Stereoscopic Vision Optimization

This research focuses on efficient methods of generating 2D maps from stereo vision in real-time. Instead of attempting to locate edges between objects, we make the assumption that the representative surfaces of objects in a view provide enough information to generate a map while taking less time to locate during processing. Since all real-time vision processing endeavors are extremely computationally intensive, numerous optimization techniques are applied to allow for a real-time application: horizontal spike smoothing for post-disparity noise, masks to focus on close-proximity objects, melding for object synthesis, and rectangular fitting for object extraction under a planar assumption. Additionally, traditional image transformation mechanisms such as rotation, translation, and scaling are integrated. Results from our research are an encouraging 10Hz with no vision post processing and accuracy up to 11 feet. Finally, vision mapping results are compared to simultaneously collected sonar data in three unique experimental settings

Laser-Based Detection and Tracking of Moving Obstacles to Improve Perception of Unmanned Ground Vehicles

El objetivo de esta tesis es desarrollar un sistema que mejore la etapa de percepción de vehículos terrestres no tripulados (UGVs) heterogéneos, consiguiendo con ello una navegación robusta en términos de seguridad y ahorro energético en diferentes entornos reales, tanto interiores como exteriores. La percepción debe tratar con obstáculos estáticos y dinámicos empleando sensores heterogéneos, tales como, odometría, sensor de distancia láser (LIDAR), unidad de medida inercial (IMU) y sistema de posicionamiento global (GPS), para obtener la información del entorno con la precisión más alta, permitiendo mejorar las etapas de planificación y evitación de obstáculos. Para conseguir este objetivo, se propone una etapa de mapeado de obstáculos dinámicos (DOMap) que contiene la información de los obstáculos estáticos y dinámicos. La propuesta se basa en una extensión del filtro de ocupación bayesiana (BOF) incluyendo velocidades no discretizadas. La detección de velocidades se obtiene con Flujo Óptico sobre una rejilla de medidas LIDAR discretizadas. Además, se gestionan las oclusiones entre obstáculos y se añade una etapa de seguimiento multi-hipótesis, mejorando la robustez de la propuesta (iDOMap). La propuesta ha sido probada en entornos simulados y reales con diferentes plataformas robóticas, incluyendo plataformas comerciales y la plataforma (PROPINA) desarrollada en esta tesis para mejorar la colaboración entre equipos de humanos y robots dentro del proyecto ABSYNTHE. Finalmente, se han propuesto métodos para calibrar la posición del LIDAR y mejorar la odometría con una IMU

Context classification for service robots

This dissertation presents a solution for environment sensing using sensor fusion techniques and a context/environment classification of the surroundings in a service robot, so it could change his behavior according to the different rea-soning outputs. As an example, if a robot knows he is outdoors, in a field environment, there can be a sandy ground, in which it should slow down. Contrariwise in indoor environments, that situation is statistically unlikely to happen (sandy ground). This simple assumption denotes the importance of context-aware in automated guided vehicles