A self-organized collective foraging method using a robot swarm

Karagüzel, Tugay Alperen
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.


Gür, Emre; Turgut, Ali Emre; Şahin, Erol; Department of Mechanical Engineering (2022-9-09)
In this thesis, the development of a social, reinforcement learning-based aggregation method is covered together with the development of a mobile robot swarm of Kobot- Tracked (Kobot-T) robots. The proposed method is developed to improve efficiency in low robot density swarm environments especially when the aggregated area is difficult to find. The method is called Social Reinforcement Learning, and Landmark-Based Aggregation (SRLA) and it is based on Q learning. In this method, robots share their Q tables ...
Collective gradient perception with a flying robot swarm
Karaguzel, Tugay Alperen; Turgut, Ali Emre; Eiben, A. E.; Ferrante, Eliseo (2022-10-01)
In this paper, we study the problem of collective and emergent sensing with a flying robot swarm in which social interactions among individuals lead to following the gradient of a scalar field in the environment without the need of any gradient sensing capability. We proposed two methods-desired distance modulation and speed modulation-with and without alignment control. In the former, individuals modulate their desired distance to their neighbors and in the latter, they modulate their speed depending on th...
Self-organized flocking in mobile robot swarms
Turgut, Ali Emre; Gökçe, Fatih; Şahin, Erol (2008-09-01)
In this paper, we study self-organized flocking in a swarm of mobile robots. We present Kobot, a mobile robot platform developed specifically for swarm robotic studies. We describe its infrared-based short range sensing system, capable of measuring the distance from obstacles and detecting kin robots, and a novel sensing system called the virtual heading system (VHS) which uses a digital compass and a wireless communication module for sensing the relative headings of neighboring robots. We propose a behavi...
Evolving self-organizing behaviors for a swarm-bot
Dorigo, M; Trianni, V; Şahin, Erol; Gross, R; Labella, TH; Baldassarre, G; Nolfi, S; Deneubourg, JL; Mondada, F; Floreano, D; Gambardella, LM (2004-09-01)
In this paper, we introduce a self-assembling and self-organizing artifact, called a swarm-bot, composed of a swarm of s-bots, mobile robots with the ability to connect to and to disconnect from each other. We discuss the challenges involved in controlling a swarm-bot and address the problem of synthesizing controllers for the swarm-bot using artificial evolution. Specifically, we study aggregation and coordinated motion of the swarm-bot using a physics-based simulation of the system. Experiments, using a s...
GESwarm Grammatical Evolution for the Automatic Synthesis of Collective Behaviors in Swarm Robotics
Ferrante, Eliseo; Turgut, Ali Emre; DuenezGuzman, Edgar; Wenseleers, Tom (2013-07-10)
In this paper we propose GESwarm, a novel tool that can automatically synthesize collective behaviors for swarms of autonomous robots through evolutionary robotics. Evolutionary robotics typically relies on artificial evolution for tuning the weights of an artificial neural network that is then used as individual behavior representation. The main caveat of neural networks is that they are very difficult to reverse engineer, meaning that once a suitable solution is found, it is very difficult to analyze, to ...
Citation Formats
T. A. Karagüzel, “A self-organized collective foraging method using a robot swarm,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Mechanical Engineering., Middle East Technical University, 2020.