Vision-based tracking of a dynamic target with application to multi-axis position control

Two sub-pixel resolution approaches to measure in-plane displacements and in-plane rotation of a known target, through image processing, are described in this research. A dynamic known target is displayed on a pixel grid, which is attached to one end of the kinematic chain of an XYθZ stage; the latter represents the experimental testbed. At the other end of the kinematic chain, a digital monochrome camera is fixed to the bottom of the stage and provides 3D position information used as the feedback signal to the vision-based control system in charge of the tool’s motion. The illuminated pixels on the display are captured in real time by the digital camera, and the stage motion control system attempts to keep the displayed image in the proper location with respect to the camera image plane. The result is a direct sensing multi-DOF position feedback system. The proposed camera-pixel grid sensing setup eliminates the reliance on the kinematic model and also avoids the need for traditional error compensation techniques, along with their associated cost and complexity. Positioning resolutions on the order of 1/100th of the pixel size on the display are achieved

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Human-Machine Interface for Remote Training of Robot Tasks

Regardless of their industrial or research application, the streamlining of robot operations is limited by the proximity of experienced users to the actual hardware. Be it massive open online robotics courses, crowd-sourcing of robot task training, or remote research on massive robot farms for machine learning, the need to create an apt remote Human-Machine Interface is quite prevalent. The paper at hand proposes a novel solution to the programming/training of remote robots employing an intuitive and accurate user-interface which offers all the benefits of working with real robots without imposing delays and inefficiency. The system includes: a vision-based 3D hand detection and gesture recognition subsystem, a simulated digital twin of a robot as visual feedback, and the "remote" robot learning/executing trajectories using dynamic motion primitives. Our results indicate that the system is a promising solution to the problem of remote training of robot tasks.Comment: Accepted in IEEE International Conference on Imaging Systems and Techniques - IST201

Cooperative Virtual Sensor for Fault Detection and Identification in Multi-UAV Applications

This paper considers the problem of fault detection and identification (FDI) in applications carried out by a group of unmanned aerial vehicles (UAVs) with visual cameras. In many cases, the UAVs have cameras mounted onboard for other applications, and these cameras can be used as bearing-only sensors to estimate the relative orientation of another UAV. The idea is to exploit the redundant information provided by these sensors onboard each of the UAVs to increase safety and reliability, detecting faults on UAV internal sensors that cannot be detected by the UAVs themselves. Fault detection is based on the generation of residuals which compare the expected position of a UAV, considered as target, with the measurements taken by one or more UAVs acting as observers that are tracking the target UAV with their cameras. Depending on the available number of observers and the way they are used, a set of strategies and policies for fault detection are defined. When the target UAV is being visually tracked by two or more observers, it is possible to obtain an estimation of its 3D position that could replace damaged sensors. Accuracy and reliability of this vision-based cooperative virtual sensor (CVS) have been evaluated experimentally in a multivehicle indoor testbed with quadrotors, injecting faults on data to validate the proposed fault detection methods.Comisión Europea H2020 644271Comisión Europea FP7 288082Ministerio de Economia, Industria y Competitividad DPI2015-71524-RMinisterio de Economia, Industria y Competitividad DPI2014-5983-C2-1-RMinisterio de Educación, Cultura y Deporte FP