10,291 research outputs found
Hand-Eye Calibration of Robonaut
NASA's Human Space Flight program depends heavily on Extra-Vehicular Activities (EVA's) performed by human astronauts. EVA is a high risk environment that requires extensive training and ground support. In collaboration with the Defense Advanced Research Projects Agency (DARPA), NASA is conducting a ground development project to produce a robotic astronaut's assistant, called Robonaut, that could help reduce human EVA time and workload. The project described in this paper designed and implemented a hand-eye calibration scheme for Robonaut, Unit A. The intent of this calibration scheme is to improve hand-eye coordination of the robot. The basic approach is to use kinematic and stereo vision measurements, namely the joint angles self-reported by the right arm and 3-D positions of a calibration fixture as measured by vision, to estimate the transformation from Robonaut's base coordinate system to its hand coordinate system and to its vision coordinate system. Two methods of gathering data sets have been developed, along with software to support each. In the first, the system observes the robotic arm and neck angles as the robot is operated under external control, and measures the 3-D position of a calibration fixture using Robonaut's stereo cameras, and logs these data. In the second, the system drives the arm and neck through a set of pre-recorded configurations, and data are again logged. Two variants of the calibration scheme have been developed. The full calibration scheme is a batch procedure that estimates all relevant kinematic parameters of the arm and neck of the robot The daily calibration scheme estimates only joint offsets for each rotational joint on the arm and neck, which are assumed to change from day to day. The schemes have been designed to be automatic and easy to use so that the robot can be fully recalibrated when needed such as after repair, upgrade, etc, and can be partially recalibrated after each power cycle. The scheme has been implemented on Robonaut Unit A and has been shown to reduce mismatch between kinematically derived positions and visually derived positions from a mean of 13.75cm using the previous calibration to means of 1.85cm using a full calibration and 2.02cm using a suboptimal but faster daily calibration. This improved calibration has already enabled the robot to more accurately reach for and grasp objects that it sees within its workspace. The system has been used to support an autonomous wrench-grasping experiment and significantly improved the workspace positioning of the hand based on visually derived wrench position. estimates
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
On the Calibration of Active Binocular and RGBD Vision Systems for Dual-Arm Robots
This paper describes a camera and hand-eye
calibration methodology for integrating an active binocular
robot head within a dual-arm robot. For this purpose, we
derive the forward kinematic model of our active robot head
and describe our methodology for calibrating and integrating
our robot head. This rigid calibration provides a closedform
hand-to-eye solution. We then present an approach for
updating dynamically camera external parameters for optimal
3D reconstruction that are the foundation for robotic tasks such
as grasping and manipulating rigid and deformable objects. We
show from experimental results that our robot head achieves
an overall sub millimetre accuracy of less than 0.3 millimetres
while recovering the 3D structure of a scene. In addition, we
report a comparative study between current RGBD cameras
and our active stereo head within two dual-arm robotic testbeds
that demonstrates the accuracy and portability of our proposed
methodology
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