On 16th of December 2015 the ASSISIbf LARICS team organized a training at the University of Zagreb, Faculty of Electrical Engineering and Computing (FER), for local students. The purpose of the training was to present, through selected lectures and a practical session, the overall goals of the ASSISIbf project and the results accomplished in the first half of the project. There were 12 students attending the training, who were divided in 4 groups of 3 students for the practical session. LARICS staff (Prof. Stjepan Bogdan, Dr. Damjan Miklić, Karlo Griparić, Tomislav Haus, Damir Mirković) was responsible for giving lectures and support during the practicals.
Prof. Bogdan gave a talk on the general concepts of the project. The students could learn about the FET programme, the FoCAS initiative, collective adaptive systems and the methodology applied within the ASSISIbf project. Dr. Miklić presented the software architecture developed for facilitating ethological experiments on honeybees. The emphasis was on distributivity and modularity of the developed framework. Students got familiar with the ZeroMessageQueue (ZMQ) framework that is used as middleware in our system. Google Protobuf was introduced to the students, as a powerful tool for message serialization/deserialization. The lectures were concluded by Karlo Griparić’s talk on the developed Combined Actuator Sensor Unit (CASU) arena. The students got familiar with the techniques that we use to design the arena, ranging from mechanical CAD design to PCB design and embedded system design.
In the practical session, the task for the students was to program CASUs. LARICS staff prepared the arena with 4 fully functional CASUs. Each group was given its own CASU to program. Bristlebots (HEXBUG Nano), tiny robots powered by a battery and vibration motor, were used to mimic the presence of bees. After the presentation of the basic CASU controller, which included the introduction of the developed Python API, the students were given 3 tasks to complete.
The first task was the calibration of infrared (IR) sensors that we use to detect bees. The second task was to control the CASU temperature and color based on the number of detected robots. In particular, if a bristlebot is detected, the temperature reference should be increased by 0.5 °C (positive feedback), and if there is no detection in a specified time period (e.g., 10s), the reference should be decreased by 0.5 °C. The color of the embedded LED should be changed with respect to the measured CASU temperature. The first two tasks were successfully completed by each group.
The final task was more ambitious and included the estimation of the number of bristlebots in the arena, based on IR detections. The students were left free to develop any kind of algorithm and it was interesting to see how they quickly employed their knowledge from machine learning. Eventually, one of the group implemented a linear regression algorithm and started the learning procedure by collecting the IR data while altering the number of bristlebots in the arena.
We hope that the insights that the students got during the training improved their programming skills and could help them in their ongoing and future projects. We also hope that we managed to familiarize students with daily activities typical for scientific projects, such as ASSISIbf, and encourage them to continue their education and start a career in science.