25 research outputs found
Active SLAM using model predictive control and attractor based exploration
Active SLAM poses the challenge for an autonomous robot to plan efficient paths simultaneous to the SLAM process. The uncertainties of the robot, map and sensor measurements, and the dynamic and motion constraints need to be considered in the planning process. In this paper, the active SLAM problem is formulated as an optimal trajectory planning problem. A novel technique is introduced that utilises an attractor combined with local planning strategies such as Model Predictive Control (a.k.a. Receding Horizon) to solve this problem. An attractor provides high level task intentions and incorporates global information about the environment for the local planner, thereby eliminating the need for costly global planning with longer horizons. It is demonstrated that trajectory planning with an attractor results in improved performance over systems that have local planning alone. Ā© 2006 IEEE
Cartographie, localisation et planification simultaneĢes āen ligneā, aĢ long terme et aĢ grande eĢchelle pour robot mobile
Pour eĢtre en mesure de naviguer dans des endroits inconnus et non structureĢs, un robot doit pouvoir cartographier lāenvironnement afin de sāy localiser. Ce probleĢme est connu sous le nom de cartographie et localisation simultaneĢes (ou SLAM pour Simultaneous Localization and Mapping). Une fois la carte de lāenvironnement creĢeĢe, des taĢches requeĢrant un deĢplacement dāun endroit connu aĢ un autre peuvent ainsi eĢtre planifieĢes. La charge de calcul du SLAM est deĢpendante de la grandeur de la carte. Un robot a une puissance de calcul embarqueĢe limiteĢe pour arriver aĢ traiter lāinformation āen ligneā, cāest-aĢ-dire aĢ bord du robot avec un temps de traitement des donneĢes moins long que le temps dāacquisition des donneĢes ou le temps maximal permis de mise aĢ jour de la carte. La navigation du robot tout en faisant le SLAM est donc limiteĢe par la taille de lāenvironnement aĢ cartographier.
Pour reĢsoudre cette probleĢmatique, lāobjectif est de deĢvelopper un algorithme de SPLAM (Simultaneous Planning Localization and Mapping) permettant la navigation peu importe la taille de lāenvironment. Pour geĢrer efficacement la charge de calcul de cet algorithme, la meĢmoire du robot est diviseĢe en une meĢmoire de travail et une meĢmoire aĢ long terme. Lorsque la contrainte de traitement āen ligneā est atteinte, les endroits vus les moins souvent et qui ne sont pas utiles pour la navigation sont transfeĢreĢes de la meĢmoire de travail aĢ la meĢmoire aĢ long terme. Les endroits transfeĢreĢs dans la meĢmoire aĢ long terme ne sont plus utiliseĢs pour la navigation. Cependant, ces endroits transfeĢreĢs peuvent eĢtre reĢcupeĢreĢes de la meĢmoire aĢ long terme aĢ la meĢmoire de travail lorsque le le robot sāapproche dāun endroit voisin encore dans la meĢmoire de travail. Le robot peut ainsi se rappeler increĢmentalement dāune partie de lāenvironment a priori oublieĢe afin de pouvoir sāy localiser pour le suivi de trajectoire.
Lāalgorithme, nommeĢ RTAB-Map, a eĢteĢ testeĢ sur le robot AZIMUT-3 dans une premieĢre expeĢrience de cartographie sur cinq sessions indeĢpendantes, afin dāeĢvaluer la capaciteĢ du systeĢme aĢ fusionner plusieurs cartes āen ligneā. La seconde expeĢrience, avec le meĢme robot utiliseĢ lors de onze sessions totalisant 8 heures de deĢplacement, a permis dāeĢvaluer la capaciteĢ du robot de naviguer de facĢ§on autonome tout en faisant du SLAM et planifier des trajectoires continuellement sur une longue peĢriode en respectant la contrainte de traitement āen ligneā . Enfin, RTAB-Map est compareĢ aĢ dāautres systeĢmes de SLAM sur quatre ensembles de donneĢes populaires pour des applications de voiture autonome (KITTI), balayage aĢ la main avec une cameĢra RGB-D (TUM RGB-D), de drone (EuRoC) et de navigation inteĢrieur avec un robot PR2 (MIT Stata Center).
Les reĢsultats montrent que RTAB-Map peut eĢtre utiliseĢ sur de longue peĢriode de temps en navigation autonome tout en respectant la contrainte de traitement āen ligneā et avec une qualiteĢ de carte comparable aux approches de lāeĢtat de lāart en SLAM visuel et avec teĢleĢmeĢtre laser. ll en reĢsulte dāun logiciel libre deĢployeĢ dans une multitude dāapplications allant des robots mobiles inteĢrieurs peu couĢteux aux voitures autonomes, en passant par les drones et la modeĢlisation 3D de lāinteĢrieur dāune maison
Active Mapping and Robot Exploration: A Survey
Simultaneous localization and mapping responds to the problem of building a map of the environment without any prior information and based on the data obtained from one or more sensors. In most situations, the robot is driven by a human operator, but some systems are capable of navigating autonomously while mapping, which is called native simultaneous localization and mapping. This strategy focuses on actively calculating the trajectories to explore the environment while building a map with a minimum error. In this paper, a comprehensive review of the research work developed in this field is provided, targeting the most relevant contributions in indoor mobile robotics.This research was funded by the ELKARTEK project ELKARBOT KK-2020/00092 of the Basque Government
A Novel Combined SLAM Based on RBPF-SLAM and EIF-SLAM for Mobile System Sensing in a Large Scale Environment
Mobile autonomous systems are very important for marine scientific investigation and military applications. Many algorithms have been studied to deal with the computational efficiency problem required for large scale Simultaneous Localization and Mapping (SLAM) and its related accuracy and consistency. Among these methods, submap-based SLAM is a more effective one. By combining the strength of two popular mapping algorithms, the Rao-Blackwellised particle filter (RBPF) and extended information filter (EIF), this paper presents a Combined SLAMāan efficient submap-based solution to the SLAM problem in a large scale environment. RBPF-SLAM is used to produce local maps, which are periodically fused into an EIF-SLAM algorithm. RBPF-SLAM can avoid linearization of the robot model during operating and provide a robust data association, while EIF-SLAM can improve the whole computational speed, and avoid the tendency of RBPF-SLAM to be over-confident. In order to further improve the computational speed in a real time environment, a binary-tree-based decision-making strategy is introduced. Simulation experiments show that the proposed Combined SLAM algorithm significantly outperforms currently existing algorithms in terms of accuracy and consistency, as well as the computing efficiency. Finally, the Combined SLAM algorithm is experimentally validated in a real environment by using the Victoria Park dataset
On Consistent Mapping in Distributed Environments using Mobile Sensors
The problem of robotic mapping, also known as simultaneous localization and mapping (SLAM), by a mobile agent for large distributed environments is addressed in this dissertation. This has sometimes been referred to as the holy grail in the robotics community, and is the stepping stone towards making a robot completely autonomous. A hybrid solution to the SLAM problem is proposed based on "first localize then map" principle. It is provably consistent and has great potential for real time application. It provides significant improvements over state-of-the-art Bayesian approaches by reducing the computational complexity of the SLAM problem without sacrificing consistency. The localization is achieved using a feature based extended Kalman filter (EKF) which utilizes a sparse set of reliable features. The common issues of data association, loop closure and computational cost of EKF based methods are kept tractable owing to the sparsity of the feature set. A novel frequentist mapping technique is proposed for estimating the dense part of the environment using the sensor observations. Given the pose estimate of the robot, this technique can consistently map the surrounding environment. The technique has linear time complexity in map components and for the case of bounded sensor noise, it is shown that the frequentist mapping technique has constant time complexity which makes it capable of estimating large distributed environments in real time. The frequentist mapping technique is a stochastic approximation algorithm and is shown to converge to the true map probabilities almost surely. The Hybrid SLAM software is developed in the C-language and is capable of handling real experimental data as well as simulations. The Hybrid SLAM technique is shown to perform well in simulations, experiments with an iRobot Create, and on standard datasets from the Robotics Data Set Repository, known as Radish. It is demonstrated that the Hybrid SLAM technique can successfully map large complex data sets in an order of magnitude less time than the time taken by the robot to acquire the data. It has low system requirements and has the potential to run on-board a robot to estimate large distributed environments in real time
Analysis of a RGB-D SLAM system using Real-Time Appearance-Based Mapping on Kbot
The Simultaneous Localization And Mapping (SLAM) problem has been a matter of
great importance and research in the area of intelligent robotics. The ability to map the
environment and locate itself on the map simultaneously is an essential tool for mobile
robots in an unknown environment. For localization, it is necessary to have maps. To
map the surroundings, localization is needed. Very much like a chicken-and-egg problem.
SLAM technology solves both the problem of localization as well of mapping together.
Looking for answers to this challenge, different approaches have been developed, i.e.
Visual SLAM (vSLAM), which is SLAM using cameras, in the case of this project, a
RGB-D camera.
In this Bachelor Project, the literature about robot navigation and the state of the art of
SLAM approaches have been reviewed in deep. The system has been setup on the one
hand, in simulation using Gazebo, and on the other hand, in a real a environment sys-
tem; more precisely, using RSAITās Kbot in the first floor of the Faculty of Informatics
(UPV/EHU). Experiments in both configurations revealed the potential of the tool for
accurately mapping the environment avoiding odometry error, and allowed to learn the
wide set of visualization tools available to ensure map correction and proper adjustment
of some parameters. The obtained maps have been used later on to command navigation
goals to the robot and to prove the usability of the learned maps
Contributions to autonomous robust navigation of mobile robots in industrial applications
151 p.Un aspecto en el que las plataformas mĆ³viles actuales se quedan atrĆ”s en comparaciĆ³n con el punto que se ha alcanzado ya en la industria es la precisiĆ³n. La cuarta revoluciĆ³n industrial trajo consigo la implantaciĆ³n de maquinaria en la mayor parte de procesos industriales, y una fortaleza de estos es su repetitividad. Los robots mĆ³viles autĆ³nomos, que son los que ofrecen una mayor flexibilidad, carecen de esta capacidad, principalmente debido al ruido inherente a las lecturas ofrecidas por los sensores y al dinamismo existente en la mayorĆa de entornos. Por este motivo, gran parte de este trabajo se centra en cuantificar el error cometido por los principales mĆ©todos de mapeado y localizaciĆ³n de robots mĆ³viles,ofreciendo distintas alternativas para la mejora del posicionamiento.Asimismo, las principales fuentes de informaciĆ³n con las que los robots mĆ³viles son capaces de realizarlas funciones descritas son los sensores exteroceptivos, los cuales miden el entorno y no tanto el estado del propio robot. Por esta misma razĆ³n, algunos mĆ©todos son muy dependientes del escenario en el que se han desarrollado, y no obtienen los mismos resultados cuando este varĆa. La mayorĆa de plataformas mĆ³viles generan un mapa que representa el entorno que les rodea, y fundamentan en este muchos de sus cĆ”lculos para realizar acciones como navegar. Dicha generaciĆ³n es un proceso que requiere de intervenciĆ³n humana en la mayorĆa de casos y que tiene una gran repercusiĆ³n en el posterior funcionamiento del robot. En la Ćŗltima parte del presente trabajo, se propone un mĆ©todo que pretende optimizar este paso para asĆ generar un modelo mĆ”s rico del entorno sin requerir de tiempo adicional para ello
NeBula: TEAM CoSTARās robotic autonomy solution that won phase II of DARPA subterranean challenge
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTARās demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.Peer ReviewedAgha, A., Otsu, K., Morrell, B., Fan, D. D., Thakker, R., Santamaria-Navarro, A., Kim, S.-K., Bouman, A., Lei, X., Edlund, J., Ginting, M. F., Ebadi, K., Anderson, M., Pailevanian, T., Terry, E., Wolf, M., Tagliabue, A., Vaquero, T. S., Palieri, M., Tepsuporn, S., Chang, Y., Kalantari, A., Chavez, F., Lopez, B., Funabiki, N., Miles, G., Touma, T., Buscicchio, A., Tordesillas, J., Alatur, N., Nash, J., Walsh, W., Jung, S., Lee, H., Kanellakis, C., Mayo, J., Harper, S., Kaufmann, M., Dixit, A., Correa, G. J., Lee, C., Gao, J., Merewether, G., Maldonado-Contreras, J., Salhotra, G., Da Silva, M. S., Ramtoula, B., Fakoorian, S., Hatteland, A., Kim, T., Bartlett, T., Stephens, A., Kim, L., Bergh, C., Heiden, E., Lew, T., Cauligi, A., Heywood, T., Kramer, A., Leopold, H. A., Melikyan, H., Choi, H. C., Daftry, S., Toupet, O., Wee, I., Thakur, A., Feras, M., Beltrame, G., Nikolakopoulos, G., Shim, D., Carlone, L., & Burdick, JPostprint (published version
NeBula: Team CoSTAR's robotic autonomy solution that won phase II of DARPA Subterranean Challenge
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTARĀæs demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.The work is partially supported by the Jet Propulsion Laboratory, California Institute of Technology,
under a contract with the National Aeronautics and Space Administration (80NM0018D0004), and
Defense Advanced Research Projects Agency (DARPA)
Formal methods for motion planning and control in dynamic and partially known environments
This thesis is motivated by time and safety critical applications involving the use of autonomous vehicles to accomplish complex tasks in dynamic and partially known environments. We use temporal logic to formally express such complex tasks. Temporal logic specifications generalize the classical notions of stability and reachability widely studied within the control and hybrid systems communities. Given a model describing the motion of a robotic system in an environment and a formal task specification, the aim is to automatically synthesize a control policy that guarantees the satisfaction of the specification. This thesis presents novel control synthesis algorithms
to tackle the problem of motion planning from temporal logic specifications in uncertain environments. For each one of the planning and control synthesis problems addressed in this dissertation, the proposed algorithms are implemented, evaluated, and validated thought experiments and/or simulations.
The first part of this thesis focuses on a mobile robot whose success is measured by the completion of temporal logic tasks within a given period of time. In addition to such time constraints, the planning algorithm must also deal with the uncertainty that arises from the changes in the robot's workspace during task execution. In particular, we consider a robot deployed in a partitioned environment subjected to structural changes such as doors that can open and close. The motion of the robot is modeled
as a continuous time Markov decision process and the robot's mission is expressed as a Continuous Stochastic Logic (CSL) formula. A complete framework to find a control strategy that satisfies a specification given as a CSL formula is introduced.
The second part of this thesis addresses the synthesis of controllers that guarantee the satisfaction of a task specification expressed as a syntactically co-safe Linear Temporal Logic (scLTL) formula. In this case, uncertainty is characterized by the partial knowledge of the robot's environment. Two scenarios are considered. First, a distributed team of robots required to satisfy the specification over a set of service requests occurring at the vertices of a known graph representing the environment is
examined. Second, a single agent motion planning problem from the specification over a set of properties known to be satised at the vertices of the known graph environment is studied. In both cases, we exploit the existence of o-the-shelf model checking and runtime verification tools, the efficiency of graph search algorithms, and the efficacy of exploration techniques to solve the motion planning problem constrained by
the absence of complete information about the environment.
The final part of this thesis extends uncertainty beyond the absence of a complete knowledge of the environment described above by considering a robot equipped with a noisy sensing system. In particular, the robot is tasked with satisfying a scLTL specification over a set of regions of interest known to be present in the environment. In such a case, although the robot is able to measure the properties characterizing such regions of interest, precisely determining the identity of these regions is not feasible. A mixed observability Markov decision process is used to represent the robot's actuation and sensing models. The control synthesis problem from scLTL
formulas is then formulated as a maximum probability reachability problem on this model. The integration of dynamic programming, formal methods, and frontier-based exploration tools allow us to derive an algorithm to solve such a reachability problem