Evolving aggregation behaviors in a swarm of robots

Trianni, V
Gross, R
Labella, TH
Şahin, Erol
Dorigo, M
In this paper, we study aggregation in a swarm of simple robots, called s-bots, having the capability to self-organize and self-assemble to form a robotic system, called a swarm-bot. The aggregation process, observed in many biological systems, is of fundamental importance since it is the prerequisite for other forms of cooperation that involve self-organization and self-assembling. We consider the problem of defining the control system for the swarm-bot using artificial evolution. The results obtained in a simulated 3D environment are presented and analyzed. They show that artificial evolution, exploiting the complex interactions among s-bots and between s-bots and the environment, is able to produce simple but general solutions to the aggregation problem.


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...
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 ...
Design of a Micro Piezo Actuated Gripper for a Swarm Robotic System
Kaygusuz, Batuhan; Turgut, Ali Emre (2016-07-31)
In this paper, we introduced a novel micro swarm robotic platform, called the AttaBot, designed specifically for complex systems and swarm robotics research. The novelty of the AttaBot platform is two-folds. (1) Artificial pheromones are implemented, (2) Each AttaBot has a gripper to grip small discs on the ground. In this way, AttaBot platform can be used in many different swarm scenarios including foraging, collective motion and collective transport. We introduced the design of the AttaBot, focusing mainl...
Swarm-Bot: Pattern Formation in A Swarm Of Self-Assembling Mobile Robots
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In this paper we introduce a new robotic system, called swarm-bot. The system consists of a swarm of mobile robots with the ability to connect to/disconnect from each other to self-assemble into different kinds of structures. First, we describe our vision and the goals of the project. Then we present preliminary results on the formation of patterns obtained from a grid-world simulation of the system.
Designing Social Cues for Collaborative Robots: The Role of Gaze and Breathing in Human-Robot Collaboration
Terzioglu, Yunus; Mutlu, Bilge; Şahin, Erol (2020-01-01)
In this paper, we investigate how collaborative robots, or cobots, typically composed of a robotic arm and a gripper carrying out manipulation tasks alongside human coworkers, can be enhanced with HRI capabilities by applying ideas and principles from character animation. To this end, we modified the appearance and behaviors of a cobot, with minimal impact on its functionality and performance, and studied the extent to which these modifications improved its communication with and perceptions by human collab...
Citation Formats
V. Trianni, R. Gross, T. Labella, E. Şahin, and M. Dorigo, “Evolving aggregation behaviors in a swarm of robots,” ADVANCES IN ARTIFICIAL LIFE, PROCEEDINGS, pp. 865–874, 2003, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55654.