By Sascha Griffiths, Ciro Natale (auth.), Florian Röhrbein, Germano Veiga, Ciro Natale (eds.)
This monograph via Florian Röhrbein, Germano Veiga and Ciro Natale is an edited choice of 15 authoritative contributions within the quarter of robotic know-how move among academia and undefined. It contains 3 components on Future business Robotics, Robotic Grasping in addition to Human-Centered Robots. The publication chapters disguise just about all the subjects these days thought of ‘hot’ in the robotics neighborhood, from trustworthy item reputation to dexterous greedy, from speech popularity to intuitive robotic programming, from cellular robotic navigation to aerial robotics, from secure actual human-robot interplay to physique extenders. All contributions stem from the result of ECHORD – the ecu Clearing apartment for Open Robotics improvement, a large-scale integrating undertaking funded by way of the ecu fee in the seventh Framework Programme from 2009 to 2013. ECHORD’s major pillars have been the so-called experiments, fifty one small-sized industry-driven learn initiatives, and the based conversation, a strong interplay device among the stakeholders. the consequences defined during this quantity are anticipated to shed new mild on innovation and know-how move from academia to within the box of robotics.
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Additional info for Gearing Up and Accelerating Cross‐fertilization between Academic and Industrial Robotics Research in Europe:: Technology Transfer Experiments from the ECHORD Project
7. Fig. 7. Resampled images from perspective of a virtual camera positioned in the center above the workplace Extraction of Current-State Mask. The first step for detecting a disruption of the safety-relevant emitted light-rays concerns the extraction of them in the camera image. Here, we pixel-wise compute the absolute differences of consecutive camera images. While the image acquisition is synced with the light emission of the projector, consecutive images differentiate remarkably at pixel positions representing safety spaces.
2 C. Vogel, C. Walter, and N. Elkmann Safety Space Generration For generating safety spacees we can differentiate between two approaches. e. the current joint angles and velocities, tthus dynamically encloses the ro obot in a minimal manner at any time, as seen in Fig. 4 aa) – c). The second approach is i the semi-static one. Here, we use a sequence of roobot states for a planned trajecttory to generate an overall safety space. Such a trajecttory defines the movement of th he robot between tow motionless positions.
Walter, and N. Elkmann Fig. 8. Computation of current-state mask (right) by differentiation of light (left) and dark image (middle) Generation of Expected-State Mask. While the current-state mask represents the pixel positions of the actual safety spaces in camera image, it is not possible to decide if there exists a disrupted light ray or not. So, we need a reference camera image that determines the pixel positions of the safety-relevant light rays in an undisrupted state. This binary virtual reference image is computed on basis of the intrinsics and extrinsics of the corresponding camera and projector and is further called expectedstate mask.