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P $$\mathrm {\Phi }$$ SS: An Open-Source Experimental Setup for Real-World Implementation of Swarm Robotic Systems in Long-Term Scenarios


Abstract and Figures

Swarm robotics is a relatively new research field that employs multiple robots (tens, hundreds or even thousands) that collaborate on complex tasks. There are several issues which limit the real-world application of swarm robotic scenarios, e.g. autonomy time, communication methods, and cost of commercialised robots. We present a platform, which aims to overcome the aforementioned limitations while using off-the-shelf components and freely-available software. The platform combines (i) a versatile open-hardware micro-robot capable of local and global communication, (ii) commercially-available wireless charging modules which provide virtually unlimited robot operation time, (iii) open-source marker-based robot tracking system for automated experiment evaluation, (iv) and a LCD display or a light projector to simulate environmental cues and pheromone communication. To demonstrate the versatility of the system, we present several scenarios, where our system was used.
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PΦSS: An Open-source Experimental Setup for
Real-world Implementation of Swarm Robotic
Systems in Long-term Scenarios?
Farshad Arvin11 [0000000179503193], Tom´s Krajn´ık2[0000000244087916] , and
Ali Emre Turgut3[0000000298371007]
1School of Electrical and Electronic Engineering, The University of Manchester,
M13 9PL, Manchester, UK,
2Artificial Intelligence Centre, Faculty of Electrical Engineering, Czech Technical
University, Prague, Czechia
3Mechanical Engineering Department, Middle East Technical University, 06800
Ankara, Turkey
Abstract. Swarm robotics is a relatively new research field that employs
multiple robots (tens, hundreds or even thousands) that collaborate
on complex tasks. There are several issues which limit the real-
world application of swarm robotic scenarios, e.g. autonomy time,
communication methods, and cost of commercialised robots. We present
a platform, which aims to overcome the aforementioned limitations
while using off-the-shelf components and freely-available software. The
platform combines (i) a versatile open-hardware micro-robot capable
of local and global communication, (ii) commercially-available wireless
charging modules which provide virtually unlimited robot operation
time, (iii) open-source marker-based robot tracking system for automated
experiment evaluation, (iv) and a LCD display or a light projector
to simulate environmental cues and pheromone communication. To
demonstrate the versatility of the system, we present several scenarios,
where our system was used.
Keywords: Open-source, Swarm Robotics, Artificial Pheromone, Per-
petual Robot Swarm, Tracking System.
1 Introduction
Swarm robotics [29], which by definition must deploy large number robots to
control them by algorithms inspired by eusocial animals, was so far not very
successful in terms of real-robot scenarios and applications. This is mainly
because maintaining a large swarm over long time requires a significant effort
which comes mostly from the fact that small robots’ batteries need to be
exchanged frequently. For large swarms, one must replace the batteries almost
?The work has been supported by UK EPSRC (Project No. EP/P01366X/1), EU
H2020 STEP2DYNA (691154), and Czech Science Foundation project 17-27006Y.
2 F.Arvin, T.Krajn´ık, A.E.Turgut
all the time, which is not only cumbersome, but it also disrupts the swarm
operation. Furthermore, many swarm experiments require that the robots know
their position or positions of other robots in their vicinity, or strength of a given
environmental cue, such as a pheromone, at their current location. This requires
the employment of a tracking system which is able to detect and localise a large
number of robots in real time.
We present a system which overcomes the aforementioned problems by
combining an affordable robotic platform, continuous charging module, real-
time large-scale localisation method and artificial pheromone system into
one experimental test-bed. This test-bed, PΦSS, allows long-term large-scale
experiments, where complex swarm behaviours emerge from interaction of a
large number of simple physical agents.
1.1 Long Term Autonomy
To tackle the problems with limited battery capacity, several different approaches
have been employed to date. The simplest, tedious way is to connect robots
with depleted batteries to their chargers manually [6], or to manually replace
their batteries [67]. These approaches do not scale well with the number of
robots and timespan of experiments. Alternatively, robots can seek charging
stations themselves [49] or schedule their charging times in accordance with
anticipated users’ demands [60, 42]. These approaches require very reliable
automated docking and they cause robots to spend a significant fraction of their
operation time at the charging location. While this can be partially resolved by
automated battery swapping systems [13], the robot has to still interrupt its
current activity, which might affect the swarm operation.
To eliminate the battery problem, one can supply the energy in a continuous
manner, e.g. by a tether [22]. While this is doable for a single robot, a tethered
multi-robot system would have to take into account the cable positions, otherwise
these will get entangled. Another researchers proposed solutions like a powered
ground [73, 75, 43,38, 66], where the robots collect energy continuously from the
ground via direct contacts. Still, the mechanical connectors get worn out and
dirty over time, which might affect the energy flow to the individual robots and
impact the behaviour of the entire swarm. Therefore, some researches [19, 36, 78]
proposed to use wireless power transfer which does not suffer from the gradual
deterioration of contact-based systems.
1.2 Tracking System
To evaluate the experiments performed or to provide the robots with their
positions in real-time, researchers typically use an external localisation system
capable of real-time tracking of multiple robots. These systems can be based
on active emitters, such as visible-light [16] or ultraviolet LEDs [72], ceiling
projection [76], radio or ultrasound beacons. A costly, high-end solution is the
commercial motion capture system from ViCon [70, 53], which is based on high-
speed and high-resolution IR (infra-red) cameras, IR emitters and reflective
PΦSS: Experimental Setup for Swarm Robotics 3
targets. Alternatively, the systems can use passive markers, such as ARTag [20],
ARToolKit+ [71] or AprilTag [51], which not only allow to determine the robot
pose, but also encode some additional information, such as robot ID. These
target detectors were used in several works in order to obtain pose information
of mobile robots [21, 55, 64, 14]. Alternative target shapes are also proposed in
recent literature, which are specifically designed for vision-based localisation
systems with a higher precision and reduced computational costs. Due to several
positive aspects, circular shaped patterns appear to be best suited as fiducial
markers in external localisation systems and can be found in several works [1,
77, 34, 44, 52].
Since the methods presented in [20, 71,51] are reported to be computationally
expensive, and [70, 53] are prohibitively expensive, we chose to use a fast open-
source system which uses a single camera to track the positions of high number
of robots in real time [39, 41]. Since the original system does not distinguish
between individual markers, we use its extensions, which use slightly modified
markers with encoded ID [5, 46, 45].
The computational efficiency of the WhyCon system was examined in [39,
45]. These comparisons show that for our application, the WhyCon system [46,
39] outperforms both the ArUco [57] and AprilTag [51] systems in terms of
computational efficiency, while providing comparable accuracy. Additionally, [39,
46] report that compared to AprilTag and Aruco, the method can use much
smaller marker size, which is also beneficial in case of robotic swarms.
1.3 Pheromones and Environmental Cues
Pheromone communication was used in swarm robotics both in simulated
[23] and real-robot scenarios for almost a decade [15]. In real-robot scenarios,
substances such as alcohol are used to emulate pheromones [58,48, 54, 59, 25].
However, these chemicals typically have different characteristics (e.g. diffusion
and evaporation) than the actual pheromones and one cannot alter these
parameters to study their influence on swarm behaviours. Furthermore, sensors
detecting these chemicals are expensive and have slow responses. To solve that,
the pheromones were simulated by means of RFID tags [31, 37], audio [3, 32]
and light [26, 8, 62, 65]. While these methods are more flexible compared to
the use of volatile chemicals, they also have certain drawbacks. For example
finite size of RFID tags limits the simulated pheromone resolution, adjustment
of the evaporation and diffusion rates of audio-based artificial pheromone
is almost impossible etc. Fortunately, light-based systems exhibit flexibility,
because light can be emitted with different intensities and colours using off-
the-shelf components such as conventional projectors [65]. In our case, we also
employ light-based artificial pheromones, which are either realised by a projector,
or an LCD screen, over which the robots move [5, 11].
4 F.Arvin, T.Krajn´ık, A.E.Turgut
2 PΦSS system description
PΦSS is a combination of four systems which have been previously developed
for swarm robotic applications. These systems are: i) open-source miniature
robot, called Mona [2], ii) perpetual swarm support system [10], iii) efficient and
accurate robot tracking module [39], and iv) artificial pheromone system [5].
This section describes the proposed experimental test-bed in detail.
2.1 Mona Robot
Mona [2] is an open-source miniature mobile robot4that has been developed for
use in swarm robotic applications. The main goal in developing the robot was
to provide a low power robot to study feasibility of Perpetual Robot Swarm [10].
Moreover, the application of the robot was expanded and recently it is also
used as an education purpose platform of mobile robotic lab for postgraduates
in control engineering. Fig. 1 shows a Mona robot’s main platform without
extension modules.
Fig. 1. Mona robot platform.
Mona has been developed based on Arduino architecture5which is a
successful open-source project mainly developed for educational purpose. Mona
adopted ATmega 328 Mini/Pro architecture hence it has the benefit of having
access to all available libraries for the Arduino module. A micro USB cable
is the only requirement to start work with the robot and connection link
for programming Mona using Arduino IDE. It is a small size robot with
diameter of 8 cm. It is actuated using two DC motors with directly attached
PΦSS: Experimental Setup for Swarm Robotics 5
wheels with diameter of 32 mm. The motors rotational speeds are controlled
independently using pulse-width modulation. Each motor has a magnetic
encoder that generates 1500 pulses per wheel revolution. It generates enough
resolution for a precise trajectory and implementing closed-loop controllers.
Mona uses a 3.7 V, 350 mAh battery which provides 3 h of autonomy.
However, it is extendible to days and months using the perpetual robot swarm
system [10] which will be presented in the following section. There were several
autonomous power transfer methods have been proposed [17, 30, 60, 50, 35, 69,
13, 7] which can be used for extending autonomy time of Mona robots deployed
in a swarm robotic scenario.
Since Mona is a low-cost robot, the platform includes only basic IR proximity
sensors to detect obstacles, walls, and other robots. Therefore, it must be a
modular platform allowing users to attach their required modules e.g. sensors and
indicators. Several modules are currently available for Mona such as ROS (Robot
Operating System) module [74] and vision board [33] for bio-inspired image
processing [24]. It is easy to develop external modules for Mona that supports
several communication approaches including I2C, RS232, SPI, and direct general
purpose digital pins.
2.2 Perpetual Swarm Support System
Perpetual robot swarm [10] is an active system which provides continuous power
to individual mobile robots without an interruption. Therefore, robots follow
their own task without considering their battery limitations. It overcomes the
limitation of robots’ battery capacity that limits the swarm scenarios to a very
short period of time.
The proposed perpetual robot swarm consists of two main parts: i) inductive
power transmitter which is connected to a main DC power source and ii) receiver
board which is connected to charging circuit of the mobile robots. At the
beginning of experiments, robots’ batteries are fully charged (4.2 V). Since the
arena is covered by many independent charging transmitters, robots harvest
small amount of power by crossing over each transmitter. Hence, they manage
to keep their battery level at the fully charge level.
A mathematical model was also proposed for the perpetual robot swarm
system which shows limitation of the system in terms of maximum speed which
robot can have to manage a perpetual system. It is directly related to forward
speed of a robot and also maximum power transmitted by the transmitters.
Several probabilistic models were proposed for modelling of swarm robotic
systems [9, 12, 63, 18, 47]. Swarm systems were also modelled with various models
e.g. a Langevin equation [28], Stock & Flow model [61], and power-law equation
model [4].
For the model that was proposed for perpetual swarm system, it was
assumed that: i) the robot is a circular with diameter of dr, ii) rectangular
individual charging cells with dimensions of xc, yc, and iii) rectangular arena
with dimensions of xa, ya. Therefore, assuming that a robot moves in a way that
6 F.Arvin, T.Krajn´ık, A.E.Turgut
the probabilistic distribution of its position inside of the arena is uniform, the
probability that it is charging is:
where ncis the number of chargers, vcis the robot speed when detecting the
charging signal, vois the robot normal speed, acand aaare the effective areas
of the arena and the charging cells respectively, tcis the coupling time of the
robot to the charging cell takes a finite time, and dccorresponds to the minimal
distance of the charging coil centre from the charging cell border. Therefore, the
only condition for robot to operate perpetually is a non-negative energy balance,
where wcis the charging power and wois the robot’s power consumption during
routine operation.
2.3 Accurate Robot Tracking
Most of the experiments performed require tracking of the individual robots,
typically for the purpose of experiment evaluation. Our tracking system is based
on an overhead camera, which provides a complete overview of the experimental
area, and a set of black-and-white markers which are attached on the robots.
The system’s core is a freely available software package capable of accurate and
efficient localisation of a large number of white-and-black circular patterns [39].
The accuracy of the system is in the order of millimetres and it is able to
track hundreds of robots in real-time. Since the experiments typically require
to distinguish the robots from each other, we had to use either the [46] variant,
which uses a Manchester coding scheme to embed an ID into the tag or the
COSΦ variant, which encodes the IDs into elliptical patterns with different
semiaxes lengths, see Fig. 2. The COSΦ tag variant, has an elliptical shape with
dimensions of 32×26 mm, and the centre of the inner, white circle is shifted by
1 mm. The the ellipse major axis allows to establish the orientation of the robot
within [90,90] and the offset of the centres is used to resolve the orientation
ambiguity. The dimensions of the inner ellipse encode the pattern ID, which
allows to distinguish individual robots. However, the COSΦ elliptical tags can
be used only in 2d with a camera directly overhead the arena. To overcome this
limitation, we implemented a more advanced variant of the tag [46, 45] which
can embed more IDs and provides a full 6 DoF pose.
While processing the camera images typically takes less than 50 µs per robot,
image capture and its transfer over USB typically takes 100 ms, which introduces
an undesirable delay. In case one needs to run experiments with rapidly-moving
robots, a specialised camera might be needed.
PΦSS: Experimental Setup for Swarm Robotics 7
Fig. 2. Original (left), COSΦ (middle) and WhyCode (right) localisation tags.
2.4 Artificial Pheromone System
COSΦ [5] is a high precision, flexible and low-cost experimental setup which
provides a reliable and user friendly platform to study bio-inspired artificial
pheromone mechanisms. The proposed system consists of: i) a visual localisation
system to detect robots [39] described in the previous section and ii) a pheromone
trail system which generates artificial pheromone and projects to the arena. The
pheromone trail display system is realised either as an LCD screen, on which
the robots move, see Fig. 3, or a projector, see Fig. 4, that illuminates the arena
from above.
Fig. 3. Artificial pheromone system realized by an LCD screen and robots with CosΦ
The system can simulate several pheromones and their interactions simul-
tanneously, where characteristics of each pheromone are determined by four
injection, ι, defines how fast a pheromone is released by a given robot,
evaporation, eφ, determines how quickly the pheromone fades over time,
diffusion, κ, defines pheromone spreading rate,
influence, c, characterises how much the pheromone affects the output image.
The output image which is a combination of multiple pheromones is
represented by a matrix I, hence brightness of a pixel at a position (x, y) is
8 F.Arvin, T.Krajn´ık, A.E.Turgut
presented as I(x, y), an ith pheromone is modelled as a matrix Φi, and the
brightness of each pixel that is projected to the arena is given by
I(x, y) =
ciΦi(x, y),(3)
where Φi(x, y) is a 2D array that represents ith pheromone intensity at position
(x, y) and cidefines the pheromone’s influence on the displayed image.
Intensity of each pheromone are continuously updated by:
Φi(x, y) = e Φi(x, y) + κi4Φi(x, y) + ιi(x, y ),(4)
where ˙
Φi(x, y) corresponds to the rate of the pheromone change caused by
its evaporation e, diffusion κiand injection ιi. To see how the interplay of
the aforementioned parameters affects the swarm behaviour, see a video of the
operational system at and
a paper [5].
2.5 Arena Configuration
The aforementioned systems were combined into a single arena, thus producing
an interactive system which can be used for various purposes e.g. group
programming, multi-system collective scenarios, evolutionary robotics etc. The
system is controlled centrally by a PC which monitors the system’s action and
applies the output signals for controlling the individual charging transmitters as
well as pheromone system.
The arena itself is a rectangle made out of 4x4 cm aluminium strut profile
with size of 120 cm x 100 cm. The arena’s floor covered by 480 independent
inductive charging transmitters. Fig. 4 shows architecture of the arena. The arena
is an active system with a continuous feedback. It means that we are able to read
the status of the charging cells whether they are in charging or idle mode. The
charging mode refers to a state which the receiver of a robot is harvesting power
and the idle mode refers to the state that transmitter is not connected to any
robot. There are two different monitoring approaches for the PΦSS system that
are: i) an overhead camera tracks robots in the arena and ii) current sensing from
individual charging cells in the perpetual swarm system. There are two outputs
from the main controller which are: i) digital images projected by the overhead
projector which are generated by the pheromone system and ii) control signals
connected to all individual transmitters which are managed by the perpetual
swarm system.
To combine COSΦ [5] which uses floor to illustrate pheromone trails on the
flat screen and perpetual swarm system [10] which also uses floor to transmit
power to the robot, a video projector was utilised for the pheromone system.
Therefore, the perpetual swarm system uses floor and the pheromone system
uses overhead video projector.
PΦSS: Experimental Setup for Swarm Robotics 9
Fig. 4. (Left) Architecture of the arena and (right) PΦSS arena made.
In terms of power management, transmitters consume very low power (about
50 mW at 12 V) when they are in the idle mode, however it reaches up to
2 W when a robot is in power harvesting mode. Therefore, the total power
consumption of the arena directly relies on the number of robots, nr, which are
deployed in an experiment: (ncnr) 50 mW nr×2 W, where ncis 480 chargers.
For the heat generated by the transmitters that, is not significant, see [10]
for more detail.
There are several interactive experimental environments which have been
developed for swarm robotic researches [26, 56, 68, 27]. However, those systems
were bespoke development for a specific application or work only with a specific
robot platform.
3 System Use Cases
The aforementioned system and its early versions, were used in a number of
scenarios investigating long-term behaviour of bio-inspired robotic swarms.
For example, in artificial pheromone communication, COSΦ [5], a flexible
communication medium for swarm and multi robotic systems using light and
its intensity has been proposed. In this communication method, some of robots
can use arena to release their messages (pheromones) and the rest of group can
detect and read those messages using their light sensors. The system allows
implementing multi-layer pheromone trails which can be used for multiple
messages e.g. alarm or food message. Thanks to the versatility of the system,
the paper [5] could investigate the impact of different pheromone settings on the
behaviour of the swarm robots.
10 F.Arvin, T.Krajn´ık, A.E.Turgut
Another scenario, where the system was deployed, concerned the impact of
dynamic and static cues on the ability of the robotic swarm to aggregate [9].
Because of the tracking system’s ability (see Section 2.3) to recover positions
of the robots relatively to the cue and number of robots in the aggregates,
experiments could be evaluated in an automatic way using the aforementioned
metrics. This allowed the authors of [9] to perform a high number of experiments,
which investigate the impact of different environmental cue settings, their
configuration, size and texture as well as the population size, robot velocity,
cue waiting time etc. The number of experiments performed allowed to gather
sufficient data required to establish a mathematical model of cue-based swarm
Finally, the full system configuration allowed to implement a perpetual robot
swarm [10], which provides a continuous power supply to miniature mobile
robots. Results of performed experiments with Mona robots [2] revealed that a
swarm can perform its collective task for more than several weeks continuously.
It allows researchers to investigate real-world very long-term scenarios on
evolutionary robotics. Currently, we are working on effects of environmental
changes on long-term collective scenario, which robots are able to learn and
update their sensing ability to adapt a dynamic environment.
4 Conclusion
In this paper we proposed a multi-purpose experimental environment for use
in swarm robotic researches The platform is completely build from off-the-
shelf components and open-source hardware and software: Mona robots [2],
wireless charging modules that allow unlimited robot operation time [10], robot
localisation module that can positions and velocities of individual robots on the
arena [40], and a LCD display or a light projector, which simulate environmental
cues and pheromone communication [5]. Using this platform, one can perfofm
long-term large-scale swarm experiments with real robots. This system opens an
opportunity to test swarm behaviours, which could be tested only in simulation.
We present three example scenarios, where the system presented demonstrated
its usefulness.
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... Mona has been developed for use in the research of swarm robotics [22]. Experiments were conducted in a rectangular arena with dimensions of 1.4 m × 0.9 m which has been developed for study on long-term swarm robotics scenarios [23]. An open-source multi-robot tracking system [24] which tracks both the position and orientation of the robot using an overhead camera was used. ...
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Accepted Manuscript - Abstract: This paper describes a versatile platform for swarm robotics research. It integrates multiple pheromone communication with a dynamic visual scene along with real time data transmission and localization of multiple-robots. The platform has been built for inquiries into social insect behavior and bio-robotics. By introducing a new research scheme to coordinate olfactory and visual cues, it not only complements current swarm robotics platforms which focus only on pheromone communications by adding visual interaction, but also may fill an important gap in closing the loop from bio-robotics to neuroscience. We have built a controllable dynamic visual environment based on our previously developed ColCOSΦ (a multi-pheromones platform) by enclosing the arena with LED panels and interacting with the micro mobile robots with a visual sensor. In addition, a wireless communication system has been developed to allow transmission of real-time bi-directional data between multiple micro robot agents and a PC host. A case study combining concepts from the internet of vehicles (IoV) and insect-vision inspired model has been undertaken to verify the applicability of the presented platform, and to investigate how complex scenarios can be facilitated by making use of this platform.
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In this paper, the feasibility of using the Robot Operating System (ROS) for controlling miniature size mobile robots was investigated. Open-source and low-cost robots employ limited processors, hence running ROS on such systems is very challenging. Therefore, we provide a compact, low-cost, and open-source module enabling miniature multi and swarm robotic systems of different sizes and types to be integrated with ROS. To investigate the feasibility of the proposed system, several experiments using a single robot and multi-robots were implemented and the results demonstrated the amenability of the system to be integrated in low-cost and open-source miniature size mobile robots.
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Swarm Robotics are widely conceived as the development of new computationally efficient tools and techniques aimed at easing and enhancing the coordination of multiple robots towards collaboratively accomplishing a certain mission or task. Among the different criteria under which the performance of Swarm Robotics can be gauged, energy efficiency and battery lifetime have played a major role in the literature. However, technological advances favoring power transfer among robots have unleashed new paradigms related to the optimization of the battery consumption considering it as a resource shared by the entire swarm. This work focuses on this context by elaborating on a routing problem for collaborative exploration in Swarm Robotics, where a subset of robots is equipped with battery recharging functionalities. Formulated as a bi-objective optimization problem, the quality of routes is measured in terms of the Pareto trade-off between the predicted area explored by robots and the risk of battery outage in the swarm. To efficiently balance these conflicting two objectives, a bio-inspired evolutionary solver is adopted and put to practice over a realistic experimental setup implemented in the VREP simulation framework. Obtained results elucidate the practicability of the proposed scheme, and suggest future research leveraging power transfer capabilities over the swarm.
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This book provides an introduction to Swarm Robotics, which is the application of methods from swarm intelligence to robotics. It goes on to present methods that allow readers to understand how to design large-scale robot systems by going through many example scenarios on topics such as aggregation, coordinated motion (flocking), task allocation, self-assembly, collective construction, and environmental monitoring. The author explains the methodology behind building multiple, simple robots and how the complexity emerges from the multiple interactions between these robots such that they are able to solve difficult tasks. The book can be used as a short textbook for specialized courses or as an introduction to Swarm Robotics for graduate students, researchers, and professionals who want a concise introduction to the field. • Provides a quick introduction to the robotics aspect of swarm intelligence; • Outlines how swarm robotics is relevant as a key technology in robotics, transport, and medicine (nanorobotics), with many references to real world examples and popular literature; • Allows readers to acquire the fundamentals of how to design and model a swarm robotic system, including relevant mathematical definitions and tools. "Given the increasing number of sophisticated robots sharing our lives, the study of how large number of robots, so-called robot swarms, interact among themselves and with fellow humans to organize their activities and perform ever more complex tasks is becoming of paramount importance. With this book on swarm robotics, Heiko Hamann gives an important contribution to the foundations of this exciting research field." Prof. Marco Dorigo, Ph.D. Directeur de Recherches du FNRS IRIDIA Université libre de Bruxelles Belgium
In this paper, the feasibility of using the Robot Operating System (ROS) for controlling miniature size mobile robots was investigated. Open-source and low-cost robots employ limited processors, hence running ROS on such systems is very challenging. Therefore, we provide a compact, low-cost, and open-source module enabling miniature multi and swarm robotic systems of different sizes and types to be integrated with ROS. To investigate the feasibility of the proposed system, several experiments using a single robot and multi-robots were implemented and the results demonstrated the amenability of the system to be integrated in low-cost and open-source miniature size mobile robots.
Shaping the collision selectivity in vision-based artificial collision-detecting systems is still an open challenge. This paper presents a novel neuron model of a locust looming detector, i.e. the lobula giant movement detector (LGMD1), in order to provide effective solutions to enhance the collision selectivity of looming objects over other visual challenges. We propose an approach to model the biologically plausible mechanisms of ON and OFF pathways and a biophysical mechanism of spike frequency adaptation (SFA) in the proposed LGMD1 visual neural network. The ON and OFF pathways can separate both dark and light looming features for parallel spatiotemporal computations. This works effectively on perceiving a potential collision from dark or light objects that approach; such a bio-plausible structure can also separate LGMD1's collision selectivity to its neighbouring looming detector-the LGMD2. The SFA mechanism can enhance the LGMD1's collision selectivity to approaching objects rather than receding and translating stimuli, which is a significant improvement compared with similar LGMD1 neuron models. The proposed framework has been tested using off-line tests of synthetic and real-world stimuli, as well as on-line bio-robotic tests. The enhanced collision selectivity of the proposed model has been validated in systematic experiments. The computational simplicity and robustness of this work have also been verified by the bio-robotic tests, which demonstrates potential in building neuromorphic sensors for collision detection in both a fast and reliable manner.