Schlagwort: ‘KI’
Automated robotic dismantling/disassembly – screw detection and removal for electric vehicle recycling
Fully automatic disassembly with precision!
Our new robotic system is revolutionizing the disassembly of electric car batteries. Using the Neura Lara 8 robot and the latest image processing technologies, we recognize screws fully automatically and position the robot precisely to remove them safely.
Thanks to the integration of YOLOv8 and Intel RealSense depth cameras, the system can locate screws in real time and position them optimally on its own. No manual intervention necessary – the system works completely autonomously!
Our goal: to make the recycling process safer, faster and more efficient. Fewer risks for workers and maximizing the recovery of raw materials at the same time. This is the future of the circular economy!
You can find more information about #dimonta here.
contact:
Markus Schmitz
Daniel Gossen
Robot Cooking – Transferring observations into a planning language
Transferring observations into a planning language: An automated approach in the field of cooking
In the Robot Cooking project, an automated method is developed to analyze and identify motion data and convert it into a machine-readable planning language. This is done using a cooking scenario as an example in which the motion data is captured by recording the hand pose of the cook.
The recording is done using a motion capture system consisting of seven cameras and a glove with three markers on the back of the chef’s hand. The position of the markers is determined by triangulation. This provides enough information to derive the hand pose. The recording is done at 120 frames per second. Before the cooking process, all objects in the workspace are identified and their initial positions determined. Motion data is continuously recorded and converted into poses with time stamps. Additional information such as velocity, acceleration and angle in relation to the tabletop are derived from the raw data.
A initial structure of the dataset is created by finding the side actions using classification. Here, pick, move and place are identified as recurrent side actions. A separate training dataset is used to train a classifier that recognizes these actions. This enables an easier analysis of the remaining actions.
Clustering is applied to identify unknown actions. A dynamic approach allows analysis despite high variability in execution. A unique fingerprint for each action is found, based on the orientation of the back of the hand and its speed on the table plane, to assign each frame to a cluster and finally to an action.
The knowledge gained from classification and clustering is translated into a machine-readable Planning Domain Definition Language (PDDL). A schedule is created, with known actions directly assigned. Start and end positions are specified, and virtual object tracking is used to represent the progression of objects during cooking. For unknown actions, preconditions and effects are handled dynamically. The results are translated into a machine-readable PDDL. This formal representation enables efficient automatic scheduling and execution of the previously demonstrated cooking task.
Additional information is available in the video linked above, the poster and the paper.
Contact:
Markus Schmitz
AI task scheduling explained
Artificial Intelligence task scheduling explained using an industry scenario.
https://youtu.be/qNDgJc1XUPM
The Automated Task Planning is intended to support the use of robots in flexible environments.
Traditional robot programming as a sub-area of work preparation processes poses great challenges to individual productions with small quantities. Automated Task Planning promises to address the problems.
In the video, in addition to the introduction and classification of Automated Task Planning, the steps required for its implementation and the benefits that result from its use are presented.
The concept was validated during research at IGMR using a simulation, which is used in the examples in the video.
Contact person:
IGMR-Seminar 11.05.2021, 16:00 – 17:00 Uhr: Task Planning, Environment Representation and Reasoning in Agricultural and Industrial Robotics
Wir freuen uns Oscar Lima vom DFKI (Deutsches Forschungszentrum für Künstliche Intelligenz) aus Osnabrück als Vortragender beim nächsten Termin der IGMR Vortragsreihe im Sommersemester 21 zu haben. Der Titel seines Vortrags lautet Task Planning, Environment Representation and Reasoning in Agricultural and Industrial Robotics.
The focus of the talk will be on DFKI Osnabrück projects. Most of our work is related to agricultural robotics, perception, environment representation, reasoning and task planning. We start the talk with the concept of precision farming, how robots can assist there, to then look into route planning, environment representation, and some of its applications in navigation and expert systems. At the end of the talk we will finish with projects that are related with industry 4.0 and one which aims to provide a generic tool for AI planning in Europe. The talk is light and conceptual, I hope to catch your attention with interesting and new ideas!
Zoom Meeting Informationen:
11.05.2021, 16:00 – 17:00 Uhr
https://rwth.zoom.us/j/95798557131?pwd=WDNoUmxLV3h4R2JTWVZWMWNSajhNZz09
Meeting-ID: 957 9855 7131
Kenncode: 917617
Die Datenschutzhinweise zur Nutzung von Zoom und eine Handreichung für Teilnehmer (Studierende) können von den Seiten des CLS der RWTH Aachen University heruntergeladen werden.
Die Veranstaltungen werden in Zusammenarbeit mit dem VDI-GPP-Arbeitskreis des Bezirksvereins Aachen durchgeführt.
