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RobSwarm: Deep reinforcement multi robot mapping
No full textProblem: A group of robots, called a swarm, is placed in an unknown environment and is supposed to explore it independently. The goal of the exploration is the creation of a common map.
Implementation
- Equipping six Kobuki robots with appropriate sensor technology, a large battery, a router and the Jetson board
- Setup of the Jetson-Boards with self-made ROS2 nodes and the set up mesh network
- Writing of launch files for the common start of all functions
- Reinforcement learning is used to train an AI that controls the swarm by selecting points for the robots to approach and navigating to them and navigating them there.
- Setting up a responsive website using Angular and the Bootstrap
Framework
ARGUS - Aerial Rescue and Geospatial Utility System
No full textARGUS is a tool for the systematic acquisition, documentation and evaluation of drone flights in rescue operations. In addition to the very fast generation of RGB and IR orthophotos, a trained AI can automatically detect fire, people and cars in the images captured by the drones. The video gives a short introduction to the Aerial Rescue and Geospatial Utility System -- ARGUS
Check out our Github repository under
https://github.com/RoblabWh/argus/
You can find the dataset on kaggle under
https://www.kaggle.com/datasets/julienmeine/rescue-object-detectio
360° and IR- Camera Drone Flight Test: Superimposition of two data sources for Post-Fire Inspection
No full textThis video highlights a recent flight test carried out in our cutting-edge robotics lab, unveiling the capabilities of our meticulously crafted thermal and 360° camera drone! We've ingeniously upgraded a DJI Avata with a bespoke thermal and 360° camera system. Compact yet powerful, measuring just 18 x 18 x 17 cm, this drone is strategically engineered to effortlessly navigate and deliver crucial thermal and 360° insights concurrently in post-fire or post-explosion environments.
The integration of a specialized thermal and 360° camera system enables the simultaneous capture of both data sources during a single flight. This groundbreaking approach not only reduces inspection time by half but also facilitates the seamless superimposition of thermal and 360° videos for comprehensive analysis and interpretation
Confidential VMs mit Intel Trust Domain Extensions
No full textVertrauenswürdigkeit ist ein immer wichtigerer Aspekt moderner IT. Intel hat die vierte Generation seiner Xeon-Enterprise-Prozessoren um die Trust Domain Extensions (TDX) erweitert. Damit lassen sich komplette VMs in einer vertrauenswürdigen Umgebung ausführen
Jdpd [Softwarecode, Version 1.6.1.0]
No full textJdpd - An open Java Simulation Kernel for Molecular Fragment Dissipative Particle Dynamics (DPD)
Jdpd is an open Java simulation kernel for Molecular Fragment Dissipative Particle Dynamics (DPD) with parallelizable force calculation, efficient caching options and fast property calculations. It is characterized by an interface and factory-pattern driven design for simple code changes and may help to avoid problems of polyglot programming. Detailed input/output communication, parallelization and process control as well as internal logging capabilities for debugging purposes are supported. The kernel may be utilized in different simulation environments ranging from flexible scripting solutions up to fully integrated “all-in-one” simulation systems like MFsim.
Since Jdpd version 1.6.1.0 Jdpd is available in a (basic) double-precision version and a (derived) single-precision version (= JdpdSP) for all numerical calculations, where the single precision version needs about half the memory of the double precision version.
Jdpd uses the Apache Commons Math and Apache Commons RNG libraries and is published as open source under the GNU General Public License version 3. This repository comprises the Java bytecode libraries (including the Apache Commons Math and RNG libraries), the Javadoc HTML documentation and the Netbeans source code packages including Unit tests.
Jdpd has been described in the scientific literature (the final manuscript 2018 - van den Broek - Jdpd - Final Manucsript.pdf is added to the repository) and used for DPD studies (see references below).
See text file JdpdVersionHistory.txt for a version history with more detailed information
A Simple Approach for Module Temperature and Power Prediction
No full textIn this work a mathematical approach to calculate solar panel temperature based on measured irradiance, temperature and wind speed is applied. With the calculated module temperature, the electrical solar module characteristics is determined. A program developed in MatLab App Designer allows to import measurement data from a weather station and calculates the module temperature based on the mathematical NOCT and stationary approach with a time step between the measurements of 5 minutes. Three commercially available solar panels with different cell and interconnection technologies are used for the verification of the established models. The results show a strong correlation between the measured and by the stationary model predicted module temperature with a coefficient of determination R2 close to 1 and a root mean square deviation (RMSE) of ≤ 2.5 K for a time period of three months. Based on the predicted temperature, measured irradiance in module plane and specific module information the program models the electrical data as time series in 5-minute steps. Predicted to measured power for a time period of three months shows a linear correlation with an R2 of 0.99 and a mean absolute error (MAE) of 3.5, 2.7 and 4.8 for module ID 1, 2 and 3. The calculated energy (exemplarily for module ID 2) based on the measured, calculated by the NOCT and stationary model for this time period is 118.4 kWh, resp. 116.7 kWh and 117.8 kWh. This is equivalent to an uncertainty of 1.4% for the NOCT and 0.5% for the stationary model
