PosterPDF Available

Self-organised Flocking in Robotic Swarm based on Active Elastic Sheet

Authors:
Self-organised Flocking in Robotic Swarm based on Active Elastic Sheet
Mohsen Raoufi, Ali Emre Turgut, Barry Lennox, Farshad Arvin
Collective motion, one of the most fascinating phenomena
observed in nature, has been aroused so much attention in
various areas such as physics, control and robotics, during the
recent decade. In most of these studies, robots use orientation
and proximity of their neighbors to achieve collective mo-
tion [1]. In such an approach, one of the biggest problems is
to measure orientation information using on-board sensors.
In the current study, we implemented a fully autonomous
coordinated motion without using alignment information.
The approach was based on Active Elastic Sheet (AES)
method [2]. In this elasticity-based method, the swarm is
modelled as a network of particles in a lattice, in which each
agent is linked to its neighbors with a spring. The force of
springs make each agent to move toward the equilibrium
point at which the total spring force is zero. The method
has some advantages, not relying on exchanging orientation
information and assuming 2-degree of freedom agents are to
name but two [3].
We, however, modified the method and added the capabil-
ity that enables the swarm to move toward a desired direction
as well as rotate about any arbitrary, time-varying point. In
addition, in order to enhance the performance, the parameters
of the modified method are optimized using Tabu Continuous
Ant Colony System (TCACS) optimization algorithm which
is a combination of Tabu Search (TS) and Continuous Ant
Colony System (CACS) optimization algorithms. In order to
define the objective function, a new criterion for measuring
the alignment of the swarm is introduced based on the desired
direction of motion.
To implement the method, we used Mona robot which
is an affordable open-source mobile robot developed for
swarm robotic applications [4]. Experiments were conducted
in different settings to show distinct abilities mentioned
earlier. Besides achieving collective motion, the modified
AES method shows its ability to lead the swarm to move
toward the desired direction and rotate about its center
without loosing the coherency. Moreover, thanks to the
linearity feature of these two motions, the swarm can rotate
about the center while moving toward the desired direction.
Two simulation platforms have been used; Matlab, where
the kinematic motion of the robots are simulated and the
optimization algorithms are performed; Webots, in which
This work was supported by EPSRC RNE (EP/P01366X/1) and EPSRC
RAIN (EP/R026084/1) projects.
M. Raoufi, B. Lennox, F. Arvin are with the School of Electrical
and Electronic Engineering, The University of Manchester, M13 9PL,
Manchester, UK, email: farshad.arvin@manchester.ac.uk
A.E. Turgut is with the Mechanical Engineering Department, Middle East
Technical University, 06800 Ankara, Turkey
(a) (b)
Fig. 1. (a) Mona, an open-source miniature mobile robot developed for
swarm robotic applications and (b) Mona model in Webots.
a 3D model of Mona robot is designed and a swarm
consisting of 100 Mona robots do the flocking. The effect of
optimization on the behavior of flocking, such as increasing
the rate of convergence and decreasing the total spring force,
was investigated (Fig. 2).
-2 -1 0 1 2
x [m]
-2
-1.5
-1
-0.5
0
0.5
y [m]
Time : 0.100 [sec]
-2 -1 0 1 2
x [m]
-2
-1.5
-1
-0.5
0
0.5
y [m]
Time : 5.100 [sec]
-2 -1 0 1 2
x [m]
-2
-1.5
-1
-0.5
0
0.5
y [m]
Time : 15.100 [sec]
-2 -1 0 1 2
x [m]
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
y [m]
Time : 0.100 [sec]
-2 -1 0 1 2
x [m]
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
y [m]
Time : 5.100 [sec]
-2 -1 0 1 2
x [m]
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
y [m]
Time : 15.100 [sec]
-2 -1 0 1 2
x [m]
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
y [m]
Time: 0.1 [Sec]
-2 -1 0 1 2
x [m]
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
y [m]
Time: 5.1 [Sec]
-2 -1 0 1 2
x [m]
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
y [m]
Time: 15.1 [Sec]
Fig. 2. Simulation Results: First row: Pure linear motion in Matlab, Second
row: Pure rotational motion in Matlab, Third row: Linear+rotational motion
in Webots
REFERENCES
[1] T. Vicsek and A. Zafeiris, “Collective motion,Physics reports, vol.
517, no. 3-4, pp. 71–140, 2012.
[2] E. Ferrante, A. E. Turgut, M. Dorigo, and C. Huepe, “Collective motion
dynamics of active solids and active crystals,New Journal of Physics,
vol. 15, no. 9, p. 095011, 2013.
[3] M. Raoufi, A. E. Turgut, and F. Arvin, “Self-organized collective motion
with a simulated real robot swarm,” arXiv preprint arXiv:1904.03230,
2019.
[4] F. Arvin, J. Espinosa, B. Bird, A. West, S. Watson, and B. Lennox,
“Mona: an affordable open-source mobile robot for education and
research,” Journal of Intelligent & Robotic Systems, pp. 1–15, 2018.
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
Mobile robots are playing a significant role in Higher Education science and engineering teaching, as they offer a flexible platform to explore and teach a wide-range of topics such as mechanics, electronics and software. Unfortunately the widespread adoption is limited by their high cost and the complexity of user interfaces and programming tools. To overcome these issues, a new affordable, adaptable and easy-to-use robotic platform is proposed. Mona is a low-cost, open-source and open-hardware mobile robot, which has been developed to be compatible with a number of standard programming environments. The robot has been successfully used for both education and research at The University of Manchester, UK.
Article
Full-text available
We introduce a simple model of self-propelled particles connected by linear springs that describes a semi-rigid formation of active agents without explicit alignment rules. The model displays a discontinuous transition at a critical noise level, below which the group self-organizes into a collectively translating or rotating state. We identify a novel elasticity-based mechanism that cascades self-propulsion energy towards lower-energy modes as responsible for such collective motion and illustrate it by computing the spectral decomposition of the elastic energy. We study the model's convergence dynamics as a function of system size and derive analytical stability conditions for the translating state in a continuous elastic sheet approximation. We explore the dynamics of a ring-shaped configuration and of local angular perturbations of an aligned state. We show that the elasticity-based mechanism achieves collective motion even in cases with heterogeneous self-propulsion speeds. Given its robustness, simplicity and ubiquity, this mechanism could play a relevant role in various biological and artificial swarms.
Article
We review the observations and the basic laws describing the essential aspects of collective motion -- being one of the most common and spectacular manifestation of coordinated behavior. Our aim is to provide a balanced discussion of the various facets of this highly multidisciplinary field, including experiments, mathematical methods and models for simulations, so that readers with a variety of background could get both the basics and a broader, more detailed picture of the field. The observations we report on include systems consisting of units ranging from macromolecules through metallic rods and robots to groups of animals and people. Some emphasis is put on models that are simple and realistic enough to reproduce the numerous related observations and are useful for developing concepts for a better understanding of the complexity of systems consisting of many simultaneously moving entities. As such, these models allow the establishing of a few fundamental principles of flocking. In particular, it is demonstrated, that in spite of considerable differences, a number of deep analogies exist between equilibrium statistical physics systems and those made of self-propelled (in most cases living) units. In both cases only a few well defined macroscopic/collective states occur and the transitions between these states follow a similar scenario, involving discontinuity and algebraic divergences.