A Self-Organized Collective Foraging Method using a Robot Swarm

Thesis Type: Postgraduate

Institution Of The Thesis: Middle East Technical University, Graduate School of Natural and Applied Sciences, Turkey

Approval Date: 2020

Thesis Language: English


Supervisor: Ali Emre Turgut


In this thesis, a collective foraging method for a swarm of aerial robots is investigated. The method is constructed by using algorithms that are designed to work in a distributed manner, by using only local information. No member in the swarm has access to global information about positions, states or environment. The environment, that robots are planned to operate in, contains a virtual scalar field which consists of grids containing constant values. The grid values indicate desired regions of the environment. By using the proposed methods, swarm forages towards the desired regions in collective manner as a cohesive and ordered group. Important point to mention is that members do not have the capability to sense the environment to do so on their own. Instead, they use information extracted from interactions with neighbouring members. This phenomenon, which has interesting examples on nature, is called collective sensing. At first, the algorithms are implemented on MATLAB environment with particle agent models and errorless movements. Later, they are implemented and tested on GAZEBO physics simulator with realistic and physics based models of robots. Finally, they are tested with real aerial robots which are modelled on GAZEBO before. The results are analyzed in terms of being a single and cohesive group without any collision in addition to the success in foraging towards desired regions of the environment and stay there as long as possible.