Paper Info
Title
Experimental capabilities and limitations of a position-based control algorithm for swarm robotics
Abstract
Achieving efficient and reliable self-organization in groups of autonomous robots is a fundamental challenge in swarm robotics. Even simple states of collective motion, such as group translation or rotation, require nontrivial algorithms, sensors, and actuators to be achieved in real-world scenarios. We study here the capabilities and limitations in controlling experimental robot swarms of a decentralized control algorithm that only requires information on the positions of neighboring agents, and not on their headings. Using swarms of e-Puck robots, we implement this algorithm in experiments and show its ability to converge to self-organized collective translation or rotation, starting from a state with random orientations. Through a simple analytical calculation, we also unveil an essential limitation of the algorithm that produces small persistent oscillations of the aligned state, related to its marginal stability. By comparing predictions and measurements, we compute the experimental noise distributions of the linear and angular robot speeds, showing that they are well described by Gaussian functions. We then implement simulations that model this noise by adding Gaussian random variables with the experimentally measured standard deviations. These simulations are performed for multiple parameter combinations and compared to experiments, showing that they provide good predictions for the expected speed and robustness of the self-organizing dynamics.
Year
DOI
Venue
2022
10.1177/1059712320930418
ADAPTIVE BEHAVIOR
Keywords
DocType
Volume
Collective motion, decentralized control, position-based models, self-organization, swarm robotics
Journal
30
Issue
ISSN
Citations 
1
1059-7123
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Yating Zheng100.34
Cristián Huepe200.34
Zhangang Han3625.18