A reliability-based particle filter for humanoid robot self-localization in Robocup Standard Platform League

This paper deals with the problem of humanoid robot localization and proposes a new method for position estimation that has been developed for the RoboCup Standard Platform League environment. Firstly, a complete vision system has been implemented in the Nao robot platform that enables the detection of relevant field markers. The detection of field markers provides some estimation of distances for the current robot position. To reduce errors in these distance measurements, extrinsic and intrinsic camera calibration procedures have been developed and described. To validate the localization algorithm, experiments covering many of the typical situations that arise during RoboCup games have been developed: ranging from degradation in position estimation to total loss of position (due to falls, ‘kidnapped robot’, or penalization). The self-localization method developed is based on the classical particle filter algorithm. The main contribution of this work is a new particle selection strategy. Our approach reduces the CPU computing time required for each iteration and so eases the limited resource availability problem that is common in robot platforms such as Nao. The experimental results show the quality of the new algorithm in terms of localization and CPU time consumption.This work has been supported by the Spanish Science and Innovation Ministry (MICINN) under the CICYT project COBAMI: DPI2011-28507-C02-01/02. The responsibility for the content remains with the authors.Munera Sánchez, E.; Muñoz Alcobendas, M.; Blanes Noguera, F.; Benet Gilabert, G.; Simó Ten, JE. (2013). A reliability-based particle filter for humanoid robot self-localization in Robocup Standard Platform League. Sensors. 13(11):14954-14983. https://doi.org/10.3390/s131114954S1495414983131

Robot localization in symmetric environment

The robot localization problem is a key problem in making truly autonomous robots. If a robot does not know where it is, it can be difficult to determine what to do next. Monte Carlo Localization as a well known localization algorithm represents a robot\u27s belief by a set of weighted samples. This set of samples approximates the posterior probability of where the robot is located. Our method presents an extension to the MCL algorithm when localizing in highly symmetrical environments; a situation where MCL is often unable to correctly track equally probable poses for the robot. The sample sets in MCL often become impoverished when samples are generated in several locations. Our approach incorporates the idea of clustering the samples and organizes them considering to their orientation. Experimental results show our method is able to successfully determine the position of the robot in symmetric environment, while ordinary MCL often fails

Localization using natural landmarks off-field for robot soccer

textLocalization is an important problem that must be resolved in order for a robot to make an estimation of its location based on observation and odometry updates. Relying on artificial landmarks such as the lines, circles, and goalposts in the robot soccer domain, current robot localization requires prior knowledge and suffers from uncertainty problems due to partial observation, and thus is less generalizable compared to human beings, who refer to their surroundings for complimentary information. To improve the certainty of the localization model, we propose a framework that recognizes orientation by actively using natural landmarks from the off-field surroundings, extracting these visual features from raw images. Our approach involves identifying visual features and natural landmarks, training with localization information to understand the surroundings, and prediction based on matching of features. This approach can increase the precision of robot orientation and improve localization accuracy by eliminating uncertain hypotheses, and in addition, it is also a general approach that can be extended and applied to other localization problems as well.Computer Science

Multi-Robot FastSLAM for Large Domains

For a robot to build a map of its surrounding area, it must have accurate position information within the area, and to obtain accurate position information within the area, the robot needs to have an accurate map of the area. This circular problem is the Simultaneous Localization and Mapping (SLAM) problem. An efficient algorithm to solve it is FastSLAM, which is based on the Rao-Blackwellized particle filter. FastSLAM solves the SLAM problem for single-robot mapping using particles to represent the posterior of the robot pose and the map. Each particle of the filter possesses its own global map which is likely to be a grid map. The memory space required for these maps poses a serious limitation to the algorithm\u27s capability when the problem space is large. The problem will only get worse if the algorithm is adapted to multi-robot mapping. This thesis presents an alternate mapping algorithm that extends the single-robot FastSLAM algorithm to a multi-robot mapping algorithm that uses Absolute Space Representations (ASR) to represent the world. But each particle still maintains a local grid to map its vicinity and periodically this grid map is converted into an ASR. An ASR expresses a world in polygons requiring only a minimal amount of memory space. By using this altered mapping strategy, the problem faced in FastSLAM when mapping a large domain can be alleviated. In this algorithm, each robot maps separately, and when two robots encounter each other they exchange range and odometry readings from their last encounter to this encounter. Each robot then sets up another filter for the other robot\u27s data and incrementally updates its own map, incorporating the passed data and its own data at the same time. The passed data is processed in reverse by the receiving robot as if a virtual robot is back-tracking the path of the other robot. The algorithm is demonstrated using three data sets collected using a single robot equipped with odometry and laser-range finder sensors

Exploring the limits of PDR-based indoor localisation systems under realistic conditions

Pedestrian Dead Reckoning (PDR) plays an important role in many (hybrid) indoor positioning systems since it enables frequent, granular position updates. However, the accumulation of errors creates a need for external error correction. In this work, we explore the limits of PDR under realistic conditions using our graph-based system as an example. For this purpose, we collect sensor data while the user performs an actual navigation task using a navigation application on a smartphone. To assess the localisation performance, we introduce a task-oriented metric based on the idea of landmark navigation: instead of specifying the error metrically, we measure the ability to determine the correct segment of an indoor route, which in turn enables the navigation system to give correct instructions. We conduct offline simulations with the collected data in order to identify situations where position tracking fails and explore different options how to mitigate the issues, e.g. through detection of special features along the user’s path or through additional sensors. Our results show that the magnetic compass is often unreliable under realistic conditions and that resetting the position at strategically chosen decision points significantly improves positioning accuracy

Three-dimensional localization and mapping of static environments by means of mobile perception

Model-based task planning is one of the main capabilities of autonomous mobile robots. Especially for model-based localization and path planning, a large-scale description of the operation environment is required. Cognitive communication between man and his machine could be based on a common, three-dimensional understanding of the environment. In the case of a personal service robot, the operation environment may comprise both indoor and outdoor spaces. In this thesis, a method for the generation of a three-dimensional geometric model for large scale, structured and natural environments is presented. The environment mapping method, which uses range images as measurement data, consists of three main phases: first, geometric features are extracted from each of the range images. Secondly, the relative coordinate transformations (i.e. registrations) between the sensor viewpoint locations, where the range data was measured, are computed. And, finally, an integrated map is formed by transforming the sub-map data into a common frame of reference. Two types of geometric features are extracted from the range images: cylinder segments (or more generally truncated cone segments) and straight-line segments. With cylinder segments tree trunks and other elongated cylindrical objects can be modeled, whereas the straight line segments correspond to the upper corners of vertical walls. The features are utilized as natural landmarks for registration computation. The presented method is tested by mapping three test sites representing structured, semi-structured and natural environments. The structured environment corresponds to a part of the premises of an office building, the semi-structured environment corresponds to the surroundings of a parking lot and the natural environment is a small forest area. The dimensions of the test sites are about 50 meters, 120 meters and 40 meters square, respectively. A simple incremental approach is used to build an integrated model for the parking lot and office corridor environments. For the principal mapping experiment, concerning the small forest area, a statistically more sound, optimal approach is applied. With respect to the feature extraction methods and the computation of the relative coordinate transformations between the viewpoints, robustness to outlier data and failure modes of the methods are discussed in more detail.reviewe

Sonar attentive underwater navigation in structured environment

One of the fundamental requirements of a persistently Autonomous Underwater Vehicle (AUV) is a robust navigation system. The success of most complex robotic tasks depends on the accuracy of a vehicle’s navigation system. In a basic form, an AUV estimates its position using an on-board navigation sensors through Dead-Reckoning (DR). However DR navigation systems tends to drift in the long run due to accumulated measurement errors. One way of mitigating this problem require the use of Simultaneous Localization and Mapping (SLAM) by concurrently mapping external environment features. The performance of a SLAM navigation system depends on the availability of enough good features in the environment. On the contrary, a typical underwater structured environment (harbour, pier or oilfield) has a limited amount of sonar features in a limited locations, hence exploitation of good features is a key for effective underwater SLAM. This thesis develops a novel attentive sonar line feature based SLAM framework that improves the performance of a SLAM navigation by steering a multibeam sonar sensor,which is mounted on a pan and tilt unit, towards feature-rich regions of the environment. A sonar salience map is generated at each vehicle pose to identify highly informative and stable regions of the environment. Results from a simulated test and real AUV experiment show an attentive SLAM performs better than a passive counterpart by repeatedly visiting good sonar landmarks

MISSION-ORIENTED HETEROGENEOUS ROBOT COOPERATION BASED ON SMART RESOURCES EXECUTION

Home environments are changing as more technological devices are used to improve daily life. The growing demand for high technology in our homes means that robot integration will soon arrive. Home devices are evolving in a connected paradigm in which data flows to perform efficient home task management. Heterogeneous home robots connected in a network can establish a workflow that complements their capabilities and so increases performance within a mission execution. This work addresses the definition and requirements of a robot-group mission in the home context. The proposed solution relies on a network of smart resources, which are defined as cyber-physical systems that provide high-level service execution. Firstly, control middleware architecture is introduced as the execution base for the Smart resources. Next, the Smart resource topology and its integration within a robotic platform are addressed. Services supplied by Smart resources manage their execution through a robot behavior architecture. Robot behavior execution is hierarchically organized through a mission definition that can be established as an individual or collective approach. Environment model and interaction tasks characterize the operation capabilities of each robot within a mission. Mission goal achievement in a heterogeneous group is enhanced through the complement of the interaction capabilities of each robot. To offer a clearer explanation, a full use case is presented in which two robots cooperate to execute a mission and the previously detailed steps are evaluated. Finally, some of the obtained results are discussed as conclusions and future works is introduced.Los entornos domésticos se encuentran sometidos a un proceso de cambio gracias al empleo de dispositivos tecnológicos que mejoran la calidad de vida de las personas. La creciente demanda de alta tecnología en los hogares señala una próxima incorporación de la robótica de servicio. Los dispositivos domésticos están evolucionando hacia un paradigma de conexión en el cual la información fluye para ofrecer una gestión más eficiente. En este entorno, robots heterogéneos conectados a la red pueden establecer un flujo de trabajo que ofreciendo nuevas soluciones y incrementando la eficiencia en la ejecución de tareas. Este trabajo aborda la definición y los requisitos necesarios para la ejecución de misiones en grupos de robots heterogéneos en entornos domésticos. La solución propuesta se apoya en una red de Smart resources, que son definidos como sistemas ciber-físicos que proporcionan servicios de alto nivel. En primer lugar, se presenta la arquitectura del middleware de control en la cual se basa la ejecución de los Smart resources. A continuación se detalla la topología de los Smart resources, así como su integración en plataformas robóticas. Los servicios proporcionados por los Smart resources gestionan su ejecución mediante una arquitectura de comportamientos para robots. La ejecución de estos comportamientos se organiza de forma jerárquica mediante la definición de una misión con un objetivo establecido de forma individual o colectiva a un grupo de robots. Dentro de una misión, las tareas de modelado e interacción con el entorno define las capacidades de operación de los robots dentro de una misión. Mediante la integración de un grupo heterogéneo de robots sus diversas capacidades son complementadas para el logro un objetivo común. A fin de caracterizar esta propuesta, los mecanismos presentados en este documento se evaluarán en detalle a lo largo de una serie experimentos en los cuales un grupo de robots heterogéneos ejecutan una misión colaborativa para alcanzar un objetivo común. Finalmente, los resultados serán discutidos a modo de conclusiones dando lugar el establecimiento de un trabajo futuro.Els entorns domèstics es troben sotmesos a un procés de canvi gràcies a l'ocupació de dispositius tecnològics que milloren la qualitat de vida de les persones. La creixent demanda d'alta tecnologia a les llars assenyala una propera incorporació de la robòtica de servei. Els dispositius domèstics estan evolucionant cap a un paradigma de connexió en el qual la informació flueix per oferir una gestió més eficient. En aquest entorn, robots heterogenis connectats a la xarxa poden establir un flux de treball que ofereix noves solucions i incrementant l'eficiència en l'execució de tasques. Aquest treball aborda la definició i els requisits necessaris per a l'execució de missions en grups de robots heterogenis en entorns domèstics. La solució proposada es recolza en una xarxa de Smart resources, que són definits com a sistemes ciber-físics que proporcionen serveis d'alt nivell. En primer lloc, es presenta l'arquitectura del middleware de control en la qual es basa l'execució dels Smart resources. A continuació es detalla la tipologia dels Smart resources, així com la seva integració en plataformes robòtiques. Els serveis proporcionats pels Smart resources gestionen la seva execució mitjançant una arquitectura de comportaments per a robots. L'execució d'aquests comportaments s'organitza de forma jeràrquica mitjançant la definició d'una missió amb un objectiu establert de forma individual o col·lectiva a un grup de robots. Dins d'una missió, les tasques de modelatge i interacció amb l'entorn defineix les capacitats d'operació dels robots dins d'una missió. Mitjançant la integració d'un grup heterogeni de robots seves diverses capacitats són complementades per a l'assoliment un objectiu comú. Per tal de caracteritzar aquesta proposta, els mecanismes presentats en aquest document s'avaluaran en detall mitjançant d'una sèrie experiments en els quals un grup de robots heterogenis executen una missió col·laborativa per aconseguir un objectiu comú. Finalment, els resultats seran discutits a manera de conclusions donant lloc a l'establiment d'un treball futur.Munera Sánchez, E. (2017). MISSION-ORIENTED HETEROGENEOUS ROBOT COOPERATION BASED ON SMART RESOURCES EXECUTION [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/88404TESI