CAD-based approach for identification of elasto-static parameters of robotic manipulators

The paper presents an approach for the identification of elasto-static parameters of a robotic manipulator using the virtual experiments in a CAD environment. It is based on the numerical processing of the data extracted from the finite element analysis results, which are obtained for isolated manipulator links. This approach allows to obtain the desired stiffness matrices taking into account the complex shape of the links, couplings between rotational/translational deflections and particularities of the joints connecting adjacent links. These matrices are integral parts of the manipulator lumped stiffness model that are widely used in robotics due to its high computational efficiency. To improve the identification accuracy, recommendations for optimal settings of the virtual experiments are given, as well as relevant statistical processing techniques are proposed. Efficiency of the developed approach is confirmed by a simulation study that shows that the accuracy in evaluating the stiffness matrix elements is about 0.1%.Comment: arXiv admin note: substantial text overlap with arXiv:0909.146

Outils pour l’identification des paramètres de raideur des robots à l’aide d’un logiciel de CAO

This report proposes a CAD-based approach for identification of the elasto-static parameters of the robotic manipulators. The main contributions are in the areas of virtual experiment planning and algorithmic data processing, which allows to obtain the stiffness matrix with required accuracy. In contrast to previous works, the developed technique operates with the deflection field produced by virtual experiments in a CAD environment. The proposed approach provides high identification accuracy (about 0.1% for the stiffness matrix element) and is able to take into account the real shape of the link, coupling between rotational/translational deflections and joint particularities. To compute the stiffness matrix, the numerical technique has been developed, and some recommendations for optimal settings of the virtual experiments are given. In order to minimize the identification errors, the statistical data processing technique was applied. The advantages of the developed approach have been confirmed by case studies dealing with the links of parallel manipulator of the Orthoglide family, for which the identification errors have been reduced to 0.1%ANR COROUSS

State and Parameter Estimation of Vehicle-Trailer Systems

Vehicle-trailer systems have different unstable modes that should be considered in their stability control, including trailer snaking, jack-knifing, and roll-over. In general, vehicle control systems require vehicle parameters and states, including geometric parameters, mass, tire forces, and side slip angles which some are not constant or can be measured economically. In a vehicle-trailer system, the trailer states and parameters such as articulation angle, trailer geometric parameters, trailer mass, trailer tire forces, and yaw rate need to be measured or identified/estimated, in addition to the unknown vehicle states/parameters. The trailer states and parameters can be measured by sensors such as Inertial Measurement Unit (IMU), wheel torque sensors, and force measurement units. However, most of these sensors are not commercially viable to be used in a vehicle or trailer due to significant extra costs. Estimation algorithms are the other tools to identify the parameters and states of the system without imposing extra costs. Accurate state and parameter estimators are needed for the development and implementation of a stability control system for a vehicle-trailer system. The main purpose of this research is to design real-time state and parameter estimation algorithms for vehicle-trailer systems. Correspondingly, a comprehensive overview of different model-based and non-model-based techniques/algorithms used for estimating vehicle-trailer states and parameters are provided. The vehicle-trailer system equations of motion are then presented and based on the presented vehicle-trailer model, the possibility of the trailer states and parameters estimation are investigated for different possible vehicle-trailer on-board sensor settings. Two different methods are proposed to estimate trailer mass for arbitrary vehicle-trailer configurations: model-based and Machine Learning (ML). The stability of the model-based estimation algorithm is analyzed, establishing the convergence of the estimation error to zero. In the proposed ML-based approach, a deep neural network is designed to estimate trailer mass. The inputs of the ML-based method are selected based on the vehicle-trailer model and are normalized by the vehicle mass, tire sizes, and geometry so that retraining of the network is not needed for different towing vehicles. The simulation and experimental results demonstrate that the trailer mass can be estimated with with acceptable computational costs. In this thesis, ultrasonic sensors along with kinematics and dynamics equations of a towing vehicle are used to develop three approaches for hitch angle estimation. The first approach is based on direct calculation of hitch angle using certain a priori geometric information and distance measurements of four Ultra sonic sensors. As the second and third approaches, kinematic and dynamic models of the vehicle-trailer system are used to develop least-square and Kalman filter based recursive hitch angle estimations. A more reliable hitch angle estimation scheme is then proposed as the integration of the algorithms developed following each of the three approaches via a switching data fusion logic. It is shown that the proposed integrated hitch angle estimation scheme can be used for any ball type trailer with a flat or symmetric V-nose frontal face without any priori information on the trailer parameters. Additionally, a new approach in estimating the lateral tire forces and hitch-forces of a vehicle-trailer system is introduced. It is shown that the proposed hitch-force estimation is independent of trailer mass and geometry. The designed lateral tire forces and hitch-force estimation algorithms can be used for any ball type trailer without any priori information on the trailer parameters. A vehicle-trailer model is proposed to design an observer for the estimation of the hitch-forces and lateral tire forces. Simulations studies in CarSim along with experimental tests are used to validate the presented method to confirm the accuracy of the developed observer. Moreover, using the vehicle-trailer lateral dynamics along with the LuGre tire model, an estimation system for the lateral velocity of a vehicle-trailer is proposed. It is shown that the proposed estimation is robust to the road conditions. An affine quadratic stability approach is used to analyze the stability of the proposed estimation. The test results confirm the accuracy of the developed estimation and convergence of the vehicle-trailer lateral velocity estimation to the actual value. Model-based and ML-based estimators are developed for estimating road angles for arbitrary vehicle-trailer configurations. The estimators are shown to be independent from road friction conditions. The model-based method employs unknown input observers on the vehicle-trailer roll and pitch dynamic models. In the proposed ML-based estimator, a recurrent neural network with Long-short-term-memory gates is designed to estimate the road angles. The inputs to the ML-based method are normalized by the vehicle wheel-base, mass, and CG height to make it applicable to any towing vehicle with the need of retraining. The simulation and experimental results justify the convergence of the road angle estimation error

Outils pour l’identification des paramètres de raideur des robots à l’aide d’un logiciel de CAO

This report proposes a CAD-based approach for identification of the elasto-static parameters of the robotic manipulators. The main contributions are in the areas of virtual experiment planning and algorithmic data processing, which allows to obtain the stiffness matrix with required accuracy. In contrast to previous works, the developed technique operates with the deflection field produced by virtual experiments in a CAD environment. The proposed approach provides high identification accuracy (about 0.1% for the stiffness matrix element) and is able to take into account the real shape of the link, coupling between rotational/translational deflections and joint particularities. To compute the stiffness matrix, the numerical technique has been developed, and some recommendations for optimal settings of the virtual experiments are given. In order to minimize the identification errors, the statistical data processing technique was applied. The advantages of the developed approach have been confirmed by case studies dealing with the links of parallel manipulator of the Orthoglide family, for which the identification errors have been reduced to 0.1%ANR COROUSS

Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression

[EN] Purpose: The development of automatic and reliable algorithms for the detection and segmentation of the vertebrae are of great importance prior to any diagnostic task. However, an important problem found to accurately segment the vertebrae is the presence of the ribs in the thoracic region. To overcome this problem, a probabilistic atlas of the spine has been developed dealing with the proximity of other structures, with a special focus on ribs suppression. Methods: The data sets used consist of Computed Tomography images corresponding to 21 patients suffering from spinal metastases. Two methods have been combined to obtain the final result: firstly, an initial segmentation is performed using a fully automatic level-set method; secondly, to refine the initial segmentation, a 3D volume indicating the probability of each voxel of belonging to the spine has been developed. In this way, a probability map is generated and deformed to be adapted to each testing case. Results: To validate the improvement obtained after applying the atlas, the Dice coefficient (DSC), the Hausdorff distance (HD), and the mean surface-to-surface distance (MSD) were used. The results showed up an average of 10 mm of improvement accuracy in terms of HD, obtaining an overall final average of 15.51 2.74 mm. Also, a global value of 91.01 3.18% in terms of DSC and a MSD of 0.66 0.25 mm were obtained. The major improvement using the atlas was achieved in the thoracic region, as ribs were almost perfectly suppressed. Conclusion: The study demonstrated that the atlas is able to detect and appropriately eliminate the ribs while improving the segmentation accuracy.The authors thank the financial support of the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under Grants TEC2012-33778 and BFU2015-64380-C2-2-R (D.M.) and DPI2013-4572-R (J.D., E.D.)Ruiz-España, S.; Domingo, J.; Díaz-Parra, A.; Dura, E.; D'ocon-Alcaniz, V.; Arana, E.; Moratal, D. (2017). 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A geometrical framework for forecasting cost uncertainty in innovative high value manufacturing.

Increasing competition and regulation are raising the pressure on manufacturing organisations to innovate their products. Innovation is fraught by significant uncertainty of whole product life cycle costs and this can lead to hesitance in investing which may result in a loss of competitive advantage. Innovative products exist when the minimum information for creating accurate cost models through contemporary forecasting methods does not exist. The scientific research challenge is that there are no forecasting methods available where cost data from only one time period suffices for their application. The aim of this research study was to develop a framework for forecasting cost uncertainty using cost data from only one time period. The developed framework consists of components that prepare minimum information for conversion into a future uncertainty range, forecast a future uncertainty range, and propagate the uncertainty range over time. The uncertainty range is represented as a vector space representing the state space of actual cost variance for 3 to n reasons, the dimensionality of that space is reduced through vector addition and a series of basic operators is applied to the aggregated vector in order to create a future state space of probable cost variance. The framework was validated through three case studies drawn from the United States Department of Defense. The novelty of the framework is found in the use of geometry to increase the amount of insights drawn from the cost data from only one time period and the propagation of cost uncertainty based on the geometric shape of uncertainty ranges. In order to demonstrate its benefits to industry, the framework was implemented at an aerospace manufacturing company for identifying potentially inaccurate cost estimates in early stages of the whole product life cycle

Articulated Statistical Shape Modelling of the Shoulder Joint

The shoulder joint is the most mobile and unstable joint in the human body. This makes it vulnerable to soft tissue pathologies and dislocation. Insight into the kinematics of the joint may enable improved diagnosis and treatment of different shoulder pathologies. Shoulder joint kinematics can be influenced by the articular geometry of the joint. The aim of this project was to develop an analysis framework for shoulder joint kinematics via the use of articulated statistical shape models (ASSMs). Articulated statistical shape models extend conventional statistical shape models by combining the shape variability of anatomical objects collected from different subjects (statistical shape models), with the physical variation of pose between the same objects (articulation). The developed pipeline involved manual annotation of anatomical landmarks selected on 3D surface meshes of scapulae and humeri and establishing dense surface correspondence across these data through a registration process. The registration was performed using a Gaussian process morphable model fitting approach. In order to register two objects separately, while keeping their shape and kinematics relationship intact, one of the objects (scapula) was fixed leaving the other (humerus) to be mobile. All the pairs of registered humeri and scapulae were brought back to their native imaged position using the inverse of the associated registration transformation. The glenohumeral rotational center and local anatomic coordinate system of the humeri and scapulae were determined using the definitions suggested by the International Society of Biomechanics. Three motions (flexion, abduction, and internal rotation) were generated using Euler angle sequences. The ASSM of the model was built using principal component analysis and validated. The validation results show that the model adequately estimated the shape and pose encoded in the training data. Developing ASSM of the shoulder joint helps to define the statistical shape and pose parameters of the gleno humeral articulating surfaces. An ASSM of the shoulder joint has potential applications in the analysis and investigation of population-wide joint posture variation and kinematics. Such analyses may include determining and quantifying abnormal articulation of the joint based on the range of motion; understanding of detailed glenohumeral joint function and internal joint measurement; and diagnosis of shoulder pathologies. Future work will involve developing a protocol for encoding the shoulder ASSM with real, rather than handcrafted, pose variation