Design, and implement a computer vision method capable to detect worker bees in a beehive
Honeybee comb mapping using a robot
Design, implement and evaluate a method that can track evolution of individual comb cells in a honeybee colony
Honeybees play a crucial role in our ecosystem, acting as irreplaceable pollinators, directly affecting the global food web and biodiversity of flowering plants. Consequently, many researchers focus on understanding the behavioral strategies and dynamics of the colonies to support their health and growth. The RoboRoyale project aims to strengthen the colony through careful interactions with the honeybee queen using miniature robots introduced to the hive. To assess the effect of these interactions and the colony's health, it is important to monitor the honeybee comb and track its development in time, which is impossible with current biology techniques. In this work, we developed an algorithm for automated long-term comb mapping) using scans of the honeybee comb collected by a moving camera. Using computer vision methods and techniques from visual mapping, we build a spatially consistent semantic map that describes the individual cells and their contents. We do this in a living colony and, therefore, have to solve problems of partial observability, irregular and sparse observations and high levels of noise. The resulting map provides us with a detailed description of the hidden state of the comb in time, including predictions of the future states. We believe that our work could prove important in gathering data and insights needed for the efforts of formulating a complete model of the honeybee colony.
Assignment:
1. Get to know the RoboRoyale observation system and the data generated by its main component [1,2].
2. Get to know the computer vision methods for classifying comb cell contents and states [3,4].
3. Get to know the robot vision methods used to create semantic maps of relevant environments.
4. Implement and integrate a method that can classify comb cell contents.
5. Merge the cells’ contents classification results into a spatially consistent model using filtering methods from mobile
robotics.
6. Integrate the map into ROS tools to achieve map visualisation common in robotics.
Result:
The resulting thesis is available for download in CVUT's dspace system.
