1,500 research outputs found

    Simultaneous maximum-likelihood calibration of odometry and sensor parameters

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    For a differential-drive mobile robot equipped with an on-board range sensor, there are six parameters to calibrate: three for the odometry (radii and distance between the wheels), and three for the pose of the sensor with respect to the robot frame. This paper describes a method for calibrating all six parameters at the same time, without the need for external sensors or devices. Moreover, it is not necessary to drive the robot along particular trajectories. The available data are the measures of the angular velocities of the wheels and the range sensor readings. The maximum-likelihood calibration solution is found in a closed form

    Efficiency Improvement of Measurement Pose Selection Techniques in Robot Calibration

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    The paper deals with the design of experiments for manipulator geometric and elastostatic calibration based on the test-pose approach. The main attention is paid to the efficiency improvement of numerical techniques employed in the selection of optimal measurement poses for calibration experiments. The advantages of the developed technique are illustrated by simulation examples that deal with the geometric calibration of the industrial robot of serial architecture

    Design of Experiments for Calibration of Planar Anthropomorphic Manipulators

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    The paper presents a novel technique for the design of optimal calibration experiments for a planar anthropomorphic manipulator with n degrees of freedom. Proposed approach for selection of manipulator configurations allows essentially improving calibration accuracy and reducing parameter identification errors. The results are illustrated by application examples that deal with typical anthropomorphic manipulators.Comment: Advanced Intelligent Mechatronics (AIM), 2011 IEEE/ASME International Conference on, Budapest : Hungary (2011

    Encoderless Gimbal Calibration of Dynamic Multi-Camera Clusters

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    Dynamic Camera Clusters (DCCs) are multi-camera systems where one or more cameras are mounted on actuated mechanisms such as a gimbal. Existing methods for DCC calibration rely on joint angle measurements to resolve the time-varying transformation between the dynamic and static camera. This information is usually provided by motor encoders, however, joint angle measurements are not always readily available on off-the-shelf mechanisms. In this paper, we present an encoderless approach for DCC calibration which simultaneously estimates the kinematic parameters of the transformation chain as well as the unknown joint angles. We also demonstrate the integration of an encoderless gimbal mechanism with a state-of-the art VIO algorithm, and show the extensions required in order to perform simultaneous online estimation of the joint angles and vehicle localization state. The proposed calibration approach is validated both in simulation and on a physical DCC composed of a 2-DOF gimbal mounted on a UAV. Finally, we show the experimental results of the calibrated mechanism integrated into the OKVIS VIO package, and demonstrate successful online joint angle estimation while maintaining localization accuracy that is comparable to a standard static multi-camera configuration.Comment: ICRA 201

    Étalonnage des machines-outils Ă  cinq axes : configuration optimisĂ©e des artefacts et de la sĂ©quence de mesure de la mĂ©thode SAMBA en vue d'une estimation efficace des erreurs gĂ©omĂ©triques

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    RÉSUMÉ Les machines-outils Ă  commande numĂ©rique (MOCN) sont assujetties Ă  plusieurs sources d’erreurs, entre autres gĂ©omĂ©triques, thermiques et dynamiques qui peuvent contribuer Ă  la dĂ©gradation de leurs performances. Une attention particuliĂšre est prĂȘtĂ©e Ă  l’usinage multi axes oĂč le mouvement simultanĂ© des axes prismatiques et rotatifs engendre une erreur de positionnement et d’orientation de l’outil par rapport au point Ă  usiner sur la piĂšce. Des moyens d’évaluations de ces erreurs et de leurs causes, Ă  des fins de maintenance et de compensation, sont alors Ă  dĂ©velopper en tenant compte des aspects Ă©conomiques, techniques et humains. Il s’agit en particulier de minimiser les temps de mesures qui rĂ©sultent en des arrĂȘts de production et par consĂ©quent des coĂ»ts indirects Ă  Ă©viter Ă  l’entreprise. Le but de la prĂ©sente thĂšse est d’amĂ©liorer la prĂ©cision d’une machine-outil Ă  cinq axes Ă  travers l’optimisation d’une technique d’étalonnage existante. En vue de prĂ©dire au mieux le comportement de la machine, l’élaboration d’une routine d’inspection adĂ©quate est nĂ©cessaire. Ceci comprend un positionnement optimal des Ă©lĂ©ments du dispositif de mesure, sous forme de billes de rĂ©fĂ©rence, ainsi qu’une planification judicieuse des poses de palpage dans l’espace de travail. Une approche analytique basĂ©e sur un algorithme d’échange pour la conception d’un plan D-optimal est adoptĂ©e pour gĂ©nĂ©rer des scĂ©narios d’étalonnage en fonction des Ă©carts gĂ©omĂ©triques Ă  estimer, modĂ©lisĂ©s sous forme de polynĂŽme, et du nombre d’inconnues dĂ©finissant le modĂšle de la machine. L’évaluation de la pertinence des tests est effectuĂ©e Ă  partir d’une Ă©tude comparative de critĂšres appelĂ©s communĂ©ment en robotique, indices d’observabilitĂ©, issus de l’analyse de la matrice jacobienne d’identification. La qualitĂ© prĂ©dictive des sĂ©quences de mesures gĂ©nĂ©rĂ©es par simulation est validĂ©e en deux Ă©tapes : la premiĂšre consiste en des expĂ©riences de rĂ©pĂ©tabilitĂ© des tests optimisĂ©s imbriquĂ©s, la deuxiĂšme est une analyse de l’incertitude sur les tests et les paramĂštres d’erreurs identifiĂ©s. Une validation par mesure directe d’une cale calibrĂ©e, montĂ©e sur la table de la machine, permet de confirmer les rĂ©sultats qualitatifs fournis par l’indice d’observabilitĂ© et ceux quantitatifs dĂ©duits de l’estimation de l’incertitude. Les rĂ©sultats montrent que les routines de vĂ©rification proposĂ©es sont capables de donner une description complĂšte de la gĂ©omĂ©trie imparfaite de la machine en incluant les Ă©carts de membrures et les Ă©carts cinĂ©matiques. Une amĂ©lioration de 55.7% de la valeur de l’indice d’observabilitĂ© est constatĂ©e par rapport Ă  celle de la stratĂ©gie de mesure utilisĂ©e prĂ©sentement dans le laboratoire.----------ABSTRACT Numerically controlled machine tools are prone to potential geometric, thermal and dynamic errors that can have a negative impact on their performance. A careful attention is paid to multi-axis machining where the simultaneous movement of prismatic and rotary axes lead to a positioning and orientation deviation of the tool relative to the workpiece. Tools for assessing these errors and their causes, for maintenance and compensation purposes, are to be developed while taking into consideration economic, technical and human aspects. In particular, this involves minimizing the measurement duration which results in production downtimes and consequently indirect costs to be avoided by the company. This thesis aims to improve the accuracy of a five-axis machine tool through the optimization of an existing calibration technique. For a better prediction of the machine tool erroneous behavior, an adequate inspection routine is sought. This includes optimal positioning of the measuring device components, i.e. master balls, as well as a wise planning of the probing poses in the working volume. An analytical approach based on an exchange algorithm for a D-optimal design is carried out to generate calibration scenarios based on the estimated geometric errors, described as ordinary polynomials, and the number of unknowns predefined in the machine model. The evaluation of the optimized tests suitability relies on a comparison of criteria, commonly known in the robotics field as observability indices and are the outcome of the identification Jacobian matrix analysis. Simulation results are validated in two steps: the first one consists of a repeatability testing of nested optimized probing sequences while the second one is an analysis of the estimated uncertainty on the overall tests and the identified error parameters. Validation via a direct measuring of a calibrated gauge block, mounted on the machine workpiece, confirms the qualitative results provided by the observability index and the quantitative ones concluded from the uncertainty estimation. The outcome suggests that the proposed geometric model updating routines enable a comprehensive description of the machine tool behavior by including location errors and error motions. An improvement of 55.7% of the observability index value is depicted with respect to the currently used measurement strategy. The optimal calibration test duration varies between 30 minutes while probing one master ball for axes location errors identification and 2 hours and 18 minutes for the estimation of both axes location errors and error motions while measuring an artefact of three master balls

    Kinematics Based Visual Localization for Skid-Steering Robots: Algorithm and Theory

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    To build commercial robots, skid-steering mechanical design is of increased popularity due to its manufacturing simplicity and unique mechanism. However, these also cause significant challenges on software and algorithm design, especially for pose estimation (i.e., determining the robot's rotation and position), which is the prerequisite of autonomous navigation. While the general localization algorithms have been extensively studied in research communities, there are still fundamental problems that need to be resolved for localizing skid-steering robots that change their orientation with a skid. To tackle this problem, we propose a probabilistic sliding-window estimator dedicated to skid-steering robots, using measurements from a monocular camera, the wheel encoders, and optionally an inertial measurement unit (IMU). Specifically, we explicitly model the kinematics of skid-steering robots by both track instantaneous centers of rotation (ICRs) and correction factors, which are capable of compensating for the complexity of track-to-terrain interaction, the imperfectness of mechanical design, terrain conditions and smoothness, and so on. To prevent performance reduction in robots' lifelong missions, the time- and location- varying kinematic parameters are estimated online along with pose estimation states in a tightly-coupled manner. More importantly, we conduct in-depth observability analysis for different sensors and design configurations in this paper, which provides us with theoretical tools in making the correct choice when building real commercial robots. In our experiments, we validate the proposed method by both simulation tests and real-world experiments, which demonstrate that our method outperforms competing methods by wide margins.Comment: 18 pages in tota

    Doctor of Philosophy

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    dissertationThe need for position and orientation information in a wide variety of applications has led to the development of equally varied methods for providing it. Amongst the alternatives, inertial navigation is a solution that o ffers self-contained operation and provides angular rate, orientation, acceleration, velocity, and position information. Until recently, the size, cost, and weight of inertial sensors has limited their use to vehicles with relatively large payload capacities and instrumentation budgets. However, the development of microelectromechanical system (MEMS) inertial sensors now o ers the possibility of using inertial measurement in smaller, even human-scale, applications. Though much progress has been made toward this goal, there are still many obstacles. While operating independently from any outside reference, inertial measurement su ers from unbounded errors that grow at rates up to cubic in time. Since the reduced size and cost of these new miniaturized sensors comes at the expense of accuracy and stability, the problem of error accumulation becomes more acute. Nevertheless, researchers have demonstrated that useful results can be obtained in real-world applications. The research presented herein provides several contributions to the development of human-scale inertial navigation. A calibration technique allowing complex sensor models to be identified using inexpensive hardware and linear solution techniques has been developed. This is shown to provide significant improvements in the accuracy of the calibrated outputs from MEMS inertial sensors. Error correction algorithms based on easily identifiable characteristics of the sensor outputs have also been developed. These are demonstrated in both one- and three-dimensional navigation. The results show significant improvements in the levels of accuracy that can be obtained using these inexpensive sensors. The algorithms also eliminate empirical, application-specific simplifications and heuristics, upon which many existing techniques have depended, and make inertial navigation a more viable solution for tracking the motion around us

    Industry-oriented Performance Measures for Design of Robot Calibration Experiment

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    The paper focuses on the accuracy improvement of geometric and elasto-static calibration of industrial robots. It proposes industry-oriented performance measures for the calibration experiment design. They are based on the concept of manipulator test-pose and referred to the end-effector location accuracy after application of the error compensation algorithm, which implements the identified parameters. This approach allows the users to define optimal measurement configurations for robot calibration for given work piece location and machining forces/torques. These performance measures are suitable for comparing the calibration plans for both simple and complex trajectories to be performed. The advantages of the developed techniques are illustrated by an example that deals with machining using robotic manipulator
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