Experiments Source Code
We plan to provide the source code of all experiments and plugins for ARK (Augmented Reality for Kilobots). Each plugin is composed of the code for the BCS (Base Control Station) and the code for the Kilobot control software.
In the paper of ARK (IEEE Robotics and Automation Letters), we show three demos: automatic ID assignment, automatic positioning, and foraging.
ARK code for Kilobot's house-keeping routines
- ID assignment functionality is already included in the ARK main BCS software (available at https://github.com/DiODeProject/KilobotArena)
- Kilobots automatic calibration functionality is already included in the ARK main BCS software (available at https://github.com/DiODeProject/KilobotArena)
- Automatic positioning code
- ARK experiment code to store the image frames and use them for offline tests
- BCS code: RecordARKframes.zip
Source code of ARK-based experiments
- Foraging demo (used in Reina et al. RA-L 2017)
- Virtual-pheromone collective foraging experiment
- The code of the paper A. Font Llenas, M.S. Talamali, X. Xu, J.A.R. Marshall, and A. Reina. used in Quality-sensitive foraging by a robot swarm through virtual pheromone trails. In Proceedings of 11th International Conference on Swarm Intelligence (ANTS 2018), LNCS 11172. Springer 2018
- source code available here: https://github.com/DiODeProject/PheromoneKilobot
- videos: http://diode.group.shef.ac.uk/FontLlenas2018.html
- Virtual-pheromone collective foraging experiment (extended and improved)
- The code of the paper M.S. Talamali, T. Bose, M. Haire, X. Xu, J.A.R. Marshall, A. Reina. Sophisticated Collective Foraging with Minimalist Agents: A Swarm Robotics Test. Swarm Intelligence 14(1):25-56, 2020.
- source code available here: https://github.com/DiODeProject/PheromoneKilobotSwarmIntell
- videos: https://www.youtube.com/playlist?list=PLCGKY9OHLZwMaGeB6cxVfxmHwhBFqKF7a
- Collective decision making (best-of-n)
- The code of the paper M.S. Talamali, A. Saha, J.A.R. Marshall, A. Reina. (coming soon)
- source code available here: https://github.com/DiODeProject/AdaptationStudy
- videos: (coming soon)