Advanced robot calibration using partial pose measurements

International audienceThe paper focuses on the calibration of serial industrial robots using partial pose measurements. In contrast to other works, the developed advanced robot calibration technique is suitable for geometrical and elastostatic calibration. The main attention is paid to the model parameters identification accuracy. To reduce the impact of measurement errors, it is proposed to use directly position measurements of several points instead of computing orientation of the end-effector. The proposed approach allows us to avoid the problem of non-homogeneity of the least-square objective, which arises in the classical identification technique with the full-pose information. The developed technique does not require any normalization and can be efficiently applied both for geometric and elastostatic identification. The advantages of a new approach are confirmed by comparison analysis that deals with the efficiency evaluation of different identification strategies. The obtained results have been successfully applied to the elastostatic parameters identification of the industrial robot employed in a machining work-cell for aerospace industry

Advanced robot calibration using partial pose measurements

The paper focuses on the calibration of serial industrial robots using partial pose measurements. In contrast to other works, the developed advanced robot calibration technique is suitable for geometrical and elastostatic calibration. The main attention is paid to the model parameters identification accuracy. To reduce the impact of measurement errors, it is proposed to use directly position measurements of several points instead of computing orientation of the end-effector. The proposed approach allows us to avoid the problem of non-homogeneity of the least-square objective, which arises in the classical identification technique with the full-pose information. The developed technique does not require any normalization and can be efficiently applied both for geometric and elastostatic identification. The advantages of a new approach are confirmed by comparison analysis that deals with the efficiency evaluation of different identification strategies. The obtained results have been successfully applied to the elastostatic parameters identification of the industrial robot employed in a machining work-cell for aerospace industry

Two-Stage Transfer Learning for Heterogeneous Robot Detection and 3D Joint Position Estimation in a 2D Camera Image using CNN

Collaborative robots are becoming more common on factory floors as well as regular environments, however, their safety still is not a fully solved issue. Collision detection does not always perform as expected and collision avoidance is still an active research area. Collision avoidance works well for fixed robot-camera setups, however, if they are shifted around, Eye-to-Hand calibration becomes invalid making it difficult to accurately run many of the existing collision avoidance algorithms. We approach the problem by presenting a stand-alone system capable of detecting the robot and estimating its position, including individual joints, by using a simple 2D colour image as an input, where no Eye-to-Hand calibration is needed. As an extension of previous work, a two-stage transfer learning approach is used to re-train a multi-objective convolutional neural network (CNN) to allow it to be used with heterogeneous robot arms. Our method is capable of detecting the robot in real-time and new robot types can be added by having significantly smaller training datasets compared to the requirements of a fully trained network. We present data collection approach, the structure of the multi-objective CNN, the two-stage transfer learning training and test results by using real robots from Universal Robots, Kuka, and Franka Emika. Eventually, we analyse possible application areas of our method together with the possible improvements.Comment: 6+n pages, ICRA 2019 submissio

Encoderless Gimbal Calibration of Dynamic Multi-Camera Clusters

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

Geometric and elastostatic calibration of robotic manipulator using partial pose measurements

International audienceThe paper deals with geometric and elastostatic calibration of robotic manipulator using partial pose measurements, which do not provide the end-effector orientation. The main attention is paid to the efficiency improvement of identification procedure. In contrast to previous works, the developed calibration technique is based on the direct measurements only. To improve the identification accuracy, it is proposed to use several reference points for each manipulator configuration. This allows avoiding the problem of non-homogeneity of the least-square objective, which arises in the classical identification technique with the full-pose information (position and orientation). Its efficiency is confirmed by the comparison analysis, which deals with the accuracy evaluation of different identification strategies. The obtained theoretical results have been successfully applied to the geometric and elastostatic calibration of serial industrial robot employed in a machining work-cell for aerospace industry

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions