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Time-of-Flight Cameras Enabling Collaborative Robots for Improved Safety in Medical Applications
(2020)
Human-robot collaboration is being used more and more in industry applications and is finding its way into medical applications. Industrial robots that are used for human-robot collaboration, cannot detect obstacles from a distance. This paper introduced the idea of using wireless technology to connect a Time-of-Flight camera to off-the-shelf industrial robots. This way, the robot can detect obstacles up to a distance of five meters. Connecting Time-of-Flight cameras to robots increases the safety in human-robot collaboration by detecting obstacles before a collision. After looking at the state of the art, the authors elaborated the different requirements for such a system. The Time-of-Flight camera from Heptagon is able to work in a range of up to five meters and can connect to the control unit of the robot via a wireless connection.
Efficient collaborative robotic applications need a combination of speed and separation monitoring, and power and force limiting operations. While most collaborative robots have built-in sensors for power and force limiting operations, there are none with built-in sensor systems for speed and separation monitoring. This paper proposes a system for speed and separation monitoring directly from the gripper of the robot. It can monitor separation distances of up to three meters. We used single-pixel Time-of-Flight sensors to measure the separation distance between the gripper and the next obstacle perpendicular to it. This is the first system capable of measuring separation distances of up to three meters directly from the robot's gripper.
In der Forschungsgruppe um Prof. Dr. Thomas Wendt werden Themen in unterschiedlichsten Bereichen von Automatisierungstechnik über funktionale Sicherheit bis hin zur 3D-gedruckten Elektronik / Sensorik behandelt. Insgesamt arbeiten vier Doktoranden und vier Mitarbeiter an der Weiterentwicklung der verschiedenen Technologien, die in diesem Artikel zusammengefasst dargestellt sind.
Differentiation between human and non-human objects can increase efficiency of human-robot collaborative applications. This paper proposes to use convolutional neural networks for classifying objects in robotic applications. The body temperature of human beings is used to classify humans and to estimate the distance to the sensor. Using image classification with convolutional neural networks it is possible to detect humans in the surroundings of a robot up to five meters distance with low-cost and low-weight thermal cameras. Using transfer learning technique we trained the GoogLeNet and MobilenetV2. Results show accuracies of 99.48 % and 99.06 % respectively.
Human-robot collaboration plays a strong role in industrial production processes. The ISO/TS 15066 defines four different methods of collaboration between humans and robots. So far, there was no robotic system available that incorporates all four collaboration methods at once. Especially for the speed and separation monitoring, there was no sensor system available that can easily be attached directly to an off-the-shelf industrial robot arm and that is capable of detecting obstacles in distances from a few millimeters up to five meters. This paper presented first results of using a 3D time-of-flight camera directly on an industrial robot arm for obstacle detection in human-robot collaboration. We attached a Visionary-T camera from SICK to the flange of a KUKA LBR iiwa 7 R800. With Matlab, we evaluated the pictures and found that it works very well for detecting obstacles in a distance range starting from 0.5 m and up to 5 m.
Sichere Detektion von Menschen in der Mensch-Roboter-Kollaboration mit Time-of-Flight Kameras
(2017)
Time-of-Flight Cameras Enabling Collaborative Robots for Improved Safety in Medical Applications
(2017)
Human-robot collaboration is being used more and more in industry applications and is finding its way into medical applications. Industrial robots that are used for human-robot collaboration, cannot detect obstacles from a distance. This paper introduced the idea of using wireless technology to connect a Time-of-Flight camera to off-the-shelf industrial robots. This way, the robot can detect obstacles up to a distance of five meters. Connecting Time-of-Flight cameras to robots increases the safety in human-robot collaboration by detecting obstacles before a collision. After looking at the state of the art, the authors elaborated the different requirements for such a system. The Time-of-Flight camera from Heptagon is able to work in a range of up to five meters and can connect to the control unit of the robot via a wireless connection.
In safety critical applications wireless technologies are not widely spread. This is mainly due to reliability and latency requirements. In this paper a new wireless architecture is presented which will allow for customizing the latency and reliability for every single participant within the network. The architecture allows for building up a network of inhomogeneous participants with different reliability and latency requirements. The used TDMA scheme with TDD as duplex method is acting gentle on resources. Therefore participants with different processing and energy resources are able to participate.