221 research outputs found

    A new information theoretic approach for appearance-based navigation of non-holonomic vehicle

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    International audienceIn this paper we propose a new way to achieve a navigation task (visual path following) for a non-holonomic vehicle. We consider an image-based navigation process. We show that it is possible to navigate along a visual path without relying on the extraction, matching and tracking of geometric visual features such as keypoint. The new proposed approach relies directly on the information (entropy) contained in the image signal. We show that it is possible to build a control law directly from the maximisation of the shared information between the current image and the next key image in the visual path. The shared information between those two images is obtained using mutual information that is known to be robust to illumination variations and occlusions. Moreover the generally complex task of features extraction and matching is avoided. Both simulations and experiments on a real vehicle are presented and show the possibilities and advantages offered by the proposed method

    Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions

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    Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research

    Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions

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    Open access articleCurrently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion,lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research

    POVNav: A Pareto-Optimal Mapless Visual Navigator

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    Mapless navigation has emerged as a promising approach for enabling autonomous robots to navigate in environments where pre-existing maps may be inaccurate, outdated, or unavailable. In this work, we propose an image-based local representation of the environment immediately around a robot to parse navigability. We further develop a local planning and control framework, a Pareto-optimal mapless visual navigator (POVNav), to use this representation and enable autonomous navigation in various challenging and real-world environments. In POVNav, we choose a Pareto-optimal sub-goal in the image by evaluating all the navigable pixels, finding a safe visual path, and generating actions to follow the path using visual servo control. In addition to providing collision-free motion, our approach enables selective navigation behavior, such as restricting navigation to select terrain types, by only changing the navigability definition in the local representation. The ability of POVNav to navigate a robot to the goal using only a monocular camera without relying on a map makes it computationally light and easy to implement on various robotic platforms. Real-world experiments in diverse challenging environments, ranging from structured indoor environments to unstructured outdoor environments such as forest trails and roads after a heavy snowfall, using various image segmentation techniques demonstrate the remarkable efficacy of our proposed framework

    Clothoid-based Planning and Control in Intelligent Vehicles (Autonomous and Manual-Assisted Driving)

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    [EN] Nowadays, there are many electronic products that incorporate elements and features coming from the research in the field of mobile robotics. For instance, the well-known vacuum cleaning robot Roomba by iRobot, which belongs to the field of service robotics, one of the most active within the sector. There are also numerous autonomous robotic systems in industrial warehouses and plants. It is the case of Autonomous Guided Vehicles (AGVs), which are able to drive completely autonomously in very structured environments. Apart from industry and consumer electronics, within the automotive field there are some devices that give intelligence to the vehicle, derived in most cases from advances in mobile robotics. In fact, more and more often vehicles incorporate Advanced Driver Assistance Systems (ADAS), such as navigation control with automatic speed regulation, lane change and overtaking assistant, automatic parking or collision warning, among other features. However, despite all the advances there are some problems that remain unresolved and can be improved. Collisions and rollovers stand out among the most common accidents of vehicles with manual or autonomous driving. In fact, it is almost impossible to guarantee driving without accidents in unstructured environments where vehicles share the space with other moving agents, such as other vehicles and pedestrians. That is why searching for techniques to improve safety in intelligent vehicles, either autonomous or manual-assisted driving, is still a trending topic within the robotics community. This thesis focuses on the design of tools and techniques for planning and control of intelligent vehicles in order to improve safety and comfort. The dissertation is divided into two parts, the first one on autonomous driving and the second one on manual-assisted driving. The main link between them is the use of clothoids as mathematical formulation for both trajectory generation and collision detection. Among the problems solved the following stand out: obstacle avoidance, rollover avoidance and advanced driver assistance to avoid collisions with pedestrians.[ES] En la actualidad se comercializan infinidad de productos de electrónica de consumo que incorporan elementos y características procedentes de avances en el sector de la robótica móvil. Por ejemplo, el conocido robot aspirador Roomba de la empresa iRobot, el cual pertenece al campo de la robótica de servicio, uno de los más activos en el sector. También hay numerosos sistemas robóticos autónomos en almacenes y plantas industriales. Es el caso de los vehículos autoguiados (AGVs), capaces de conducir de forma totalmente autónoma en entornos muy estructurados. Además de en la industria y en electrónica de consumo, dentro del campo de la automoción también existen dispositivos que dotan de cierta inteligencia al vehículo, derivados la mayoría de las veces de avances en robótica móvil. De hecho, cada vez con mayor frecuencia los vehículos incorporan sistemas avanzados de asistencia al conductor (ADAS por sus siglas en inglés), tales como control de navegación con regulación automática de velocidad, asistente de cambio de carril y adelantamiento, aparcamiento automático o aviso de colisión, entre otras prestaciones. No obstante, pese a todos los avances siguen existiendo problemas sin resolver y que pueden mejorarse. La colisión y el vuelco destacan entre los accidentes más comunes en vehículos con conducción tanto manual como autónoma. De hecho, la dificultad de conducir en entornos desestructurados compartiendo el espacio con otros agentes móviles, tales como coches o personas, hace casi imposible garantizar la conducción sin accidentes. Es por ello que la búsqueda de técnicas para mejorar la seguridad en vehículos inteligentes, ya sean de conducción autónoma o manual asistida, es un tema que siempre está en auge en la comunidad robótica. La presente tesis se centra en el diseño de herramientas y técnicas de planificación y control de vehículos inteligentes, para la mejora de la seguridad y el confort. La disertación se ha dividido en dos partes, la primera sobre conducción autónoma y la segunda sobre conducción manual asistida. El principal nexo de unión es el uso de clotoides como elemento de generación de trayectorias y detección de colisiones. Entre los problemas que se resuelven destacan la evitación de obstáculos, la evitación de vuelcos y la asistencia avanzada al conductor para evitar colisiones con peatones.[CA] En l'actualitat es comercialitzen infinitat de productes d'electrònica de consum que incorporen elements i característiques procedents d'avanços en el sector de la robòtica mòbil. Per exemple, el conegut robot aspirador Roomba de l'empresa iRobot, el qual pertany al camp de la robòtica de servici, un dels més actius en el sector. També hi ha nombrosos sistemes robòtics autònoms en magatzems i plantes industrials. És el cas dels vehicles autoguiats (AGVs), els quals són capaços de conduir de forma totalment autònoma en entorns molt estructurats. A més de en la indústria i en l'electrònica de consum, dins el camp de l'automoció també existeixen dispositius que doten al vehicle de certa intel·ligència, la majoria de les vegades derivats d'avanços en robòtica mòbil. De fet, cada vegada amb més freqüència els vehicles incorporen sistemes avançats d'assistència al conductor (ADAS per les sigles en anglés), com ara control de navegació amb regulació automàtica de velocitat, assistent de canvi de carril i avançament, aparcament automàtic o avís de col·lisió, entre altres prestacions. No obstant això, malgrat tots els avanços segueixen existint problemes sense resoldre i que poden millorar-se. La col·lisió i la bolcada destaquen entre els accidents més comuns en vehicles amb conducció tant manual com autònoma. De fet, la dificultat de conduir en entorns desestructurats compartint l'espai amb altres agents mòbils, tals com cotxes o persones, fa quasi impossible garantitzar la conducció sense accidents. És per això que la recerca de tècniques per millorar la seguretat en vehicles intel·ligents, ja siguen de conducció autònoma o manual assistida, és un tema que sempre està en auge a la comunitat robòtica. La present tesi es centra en el disseny d'eines i tècniques de planificació i control de vehicles intel·ligents, per a la millora de la seguretat i el confort. La dissertació s'ha dividit en dues parts, la primera sobre conducció autònoma i la segona sobre conducció manual assistida. El principal nexe d'unió és l'ús de clotoides com a element de generació de trajectòries i detecció de col·lisions. Entre els problemes que es resolen destaquen l'evitació d'obstacles, l'evitació de bolcades i l'assistència avançada al conductor per evitar col·lisions amb vianants.Girbés Juan, V. (2016). Clothoid-based Planning and Control in Intelligent Vehicles (Autonomous and Manual-Assisted Driving) [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/65072TESI

    飛行ロボットにおける人間・ロボットインタラクションの実現に向けて : ユーザー同伴モデルとセンシングインターフェース

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    学位の種別: 課程博士審査委員会委員 : (主査)東京大学准教授 矢入 健久, 東京大学教授 堀 浩一, 東京大学教授 岩崎 晃, 東京大学教授 土屋 武司, 東京理科大学教授 溝口 博University of Tokyo(東京大学

    Exploration autonome et efficiente de chantiers miniers souterrains inconnus avec un drone filaire

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    Abstract: Underground mining stopes are often mapped using a sensor located at the end of a pole that the operator introduces into the stope from a secure area. The sensor emits laser beams that provide the distance to a detected wall, thus creating a 3D map. This produces shadow zones and a low point density on the distant walls. To address these challenges, a research team from the Université de Sherbrooke is designing a tethered drone equipped with a rotating LiDAR for this mission, thus benefiting from several points of view. The wired transmission allows for unlimited flight time, shared computing, and real-time communication. For compatibility with the movement of the drone after tether entanglements, the excess length is integrated into an onboard spool, contributing to the drone payload. During manual piloting, the human factor causes problems in the perception and comprehension of a virtual 3D environment, as well as the execution of an optimal mission. This thesis focuses on autonomous navigation in two aspects: path planning and exploration. The system must compute a trajectory that maps the entire environment, minimizing the mission time and respecting the maximum onboard tether length. Path planning using a Rapidly-exploring Random Tree (RRT) quickly finds a feasible path, but the optimization is computationally expensive and the performance is variable and unpredictable. Exploration by the frontier method is representative of the space to be explored and the path can be optimized by solving a Traveling Salesman Problem (TSP) but existing techniques for a tethered drone only consider the 2D case and do not optimize the global path. To meet these challenges, this thesis presents two new algorithms. The first one, RRT-Rope, produces an equal or shorter path than existing algorithms in a significantly shorter computation time, up to 70% faster than the next best algorithm in a representative environment. A modified version of RRT-connect computes a feasible path, shortened with a deterministic technique that takes advantage of previously added intermediate nodes. The second algorithm, TAPE, is the first 3D cavity exploration method that focuses on minimizing mission time and unwound tether length. On average, the overall path is 4% longer than the method that solves the TSP, but the tether remains under the allowed length in 100% of the simulated cases, compared to 53% with the initial method. The approach uses a 2-level hierarchical architecture: global planning solves a TSP after frontier extraction, and local planning minimizes the path cost and tether length via a decision function. The integration of these two tools in the NetherDrone produces an intelligent system for autonomous exploration, with semi-autonomous features for operator interaction. This work opens the door to new navigation approaches in the field of inspection, mapping, and Search and Rescue missions.La cartographie des chantiers miniers souterrains est souvent réalisée à l’aide d’un capteur situé au bout d’une perche que l’opérateur introduit dans le chantier, depuis une zone sécurisée. Le capteur émet des faisceaux laser qui fournissent la distance à un mur détecté, créant ainsi une carte en 3D. Ceci produit des zones d’ombres et une faible densité de points sur les parois éloignées. Pour relever ces défis, une équipe de recherche de l’Université de Sherbrooke conçoit un drone filaire équipé d’un LiDAR rotatif pour cette mission, bénéficiant ainsi de plusieurs points de vue. La transmission filaire permet un temps de vol illimité, un partage de calcul et une communication en temps réel. Pour une compatibilité avec le mouvement du drone lors des coincements du fil, la longueur excédante est intégrée dans une bobine embarquée, qui contribue à la charge utile du drone. Lors d’un pilotage manuel, le facteur humain entraîne des problèmes de perception et compréhension d’un environnement 3D virtuel, et d’exécution d’une mission optimale. Cette thèse se concentre sur la navigation autonome sous deux aspects : la planification de trajectoire et l’exploration. Le système doit calculer une trajectoire qui cartographie l’environnement complet, en minimisant le temps de mission et en respectant la longueur maximale de fil embarquée. La planification de trajectoire à l’aide d’un Rapidly-exploring Random Tree (RRT) trouve rapidement un chemin réalisable, mais l’optimisation est coûteuse en calcul et la performance est variable et imprévisible. L’exploration par la méthode des frontières est représentative de l’espace à explorer et le chemin peut être optimisé en résolvant un Traveling Salesman Problem (TSP), mais les techniques existantes pour un drone filaire ne considèrent que le cas 2D et n’optimisent pas le chemin global. Pour relever ces défis, cette thèse présente deux nouveaux algorithmes. Le premier, RRT-Rope, produit un chemin égal ou plus court que les algorithmes existants en un temps de calcul jusqu’à 70% plus court que le deuxième meilleur algorithme dans un environnement représentatif. Une version modifiée de RRT-connect calcule un chemin réalisable, raccourci avec une technique déterministe qui tire profit des noeuds intermédiaires préalablement ajoutés. Le deuxième algorithme, TAPE, est la première méthode d’exploration de cavités en 3D qui minimise le temps de mission et la longueur du fil déroulé. En moyenne, le trajet global est 4% plus long que la méthode qui résout le TSP, mais le fil reste sous la longueur autorisée dans 100% des cas simulés, contre 53% avec la méthode initiale. L’approche utilise une architecture hiérarchique à 2 niveaux : la planification globale résout un TSP après extraction des frontières, et la planification locale minimise le coût du chemin et la longueur de fil via une fonction de décision. L’intégration de ces deux outils dans le NetherDrone produit un système intelligent pour l’exploration autonome, doté de fonctionnalités semi-autonomes pour une interaction avec l’opérateur. Les travaux réalisés ouvrent la porte à de nouvelles approches de navigation dans le domaine des missions d’inspection, de cartographie et de recherche et sauvetage
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