Student’s projects at Mobots/Learnge

Students looking for a project

Semester or diploma projects for current EPFL students. We are an interdisciplinary robotics lab, meaning our projects are often for students from Section-MT, EL or ME but also frequently suitable for students in other domains.

As the section management on iS-Academia is incomplete at this time please check in the project’s description if it is available for your section before applying.

  Related sections SV, PH, MA, SC and IC
  Type Master project
  Style Software experimental
  First assistant Rafael Barmak
  Second assistant Robert Matthew Mills

The winter period can be considered the most critical time of the year for a bee colony, where approximately 50% of colonies don’t survive. In the research project HIVEOPOLIS, we are developing a smart hive including detailed sensing that aims to better understand honeybees and support them. In recent winter seasons, a robotic device was used to collect data and interact with specific collective behaviours of groups of thousands of bees.

We aim to better understand how this robotic system can interact with an animal society, and predict how animals will behave under specific environmental conditions. Thus, we propose a project focused on the development of agent-based models that will take into account specific behaviors of winter bees (e.g., movement over thermal gradients, heat production, clustering/aggregation, etc.) and some physical properties of the hive (e.g., heat propagation through honeycombs). Examples of target predictions include temperature profiles, collective metabolism, collective location and consumption of the food supplies. The model should also cater for the sensing and control of the robotic system, thereby bringing together the physical and control elements of the artificial system with the natural system.

In order to extract behavioral parameters and compare simulation results and model accuracy, the student will have access to data collected from real hives from different instrument types (e.g., cameras and temperature sensors).

Proposed Work Packages
  1 Study phase
    Winter bee individual and collective behaviors
    Computational models to simulate large numbers of agents
    Numerical methods on heat propagation within homogeneous materials
  2 Implement an agent-based model of winter honeybees that takes into account the ambient temperature and the influence of thermal actuators (Python scientific stack strongly preferred)
  3 Analyze and compare simulation results with data collected from real hives
  4 Report writing
  Related sections EL, MT, SC and IN 
  Type Semester project (10 ECTS), contact us to discuss adapting for a diploma (30 ECTS)
  Style 75% computational, 25% theoretical
  First assistant Mattieu Broisin
  Second assistant Robert Matthew Mills

The HIVEOPOLIS project is developing several robotic systems to monitor and interact with honeybees inside their hives, aiming to better understand and support these fascinating superorganisms. Various prior works have shown that vibrations are important in bee-bee communication, as well as being useful for colony state identification including important health indicators. We have incorporated vibration sensors into honeybee hives to gather vibration data, while simultaneously recording images to see what the bee colony is doing.


This semester project aims to develop a signal processing pipeline to apply to vibrational data from inside beehives. To provide “ground truth” information on what behaviours are generating given vibration patterns, image-based data is available, and will require some level of manual or automated processing.  The goal of the signal processing is to identify signatures corresponding to specific individual and group-level bee behaviours.

Work packages
  • Become familiar with literature on vibrations known in honeybees, as well as typical analysis methods
  • Implement time/frequency analysis on 1,2, and 3-channel data; and on multi-sensor data
  • Implement signal-noise reduction via multi-sensor data
  • Extract ground-truth behavioural state information from video data (likely a combination of computer vision and manual labelling)
  • Compare vibration-derived outputs to ground-truth outputs using appropriate metrics

We can support either one or two students working on this project, e.g. by partitioning the target behaviours that each student aims to investigate.

Desirable Skills
  • Taken classes on digital signal processing, pattern recognition,  statistical inference, applied data analysis
  • Interest in honeybees, ecosystems, or agriculture

For Mobots or Learn staff

To submit a project please consult: