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Ant Intelligent Robot: A Versatile and Low Cost Miniature
Mobile Robot Platform for Swarm Robotics Research and
Education
Dan M. Novischi
University Politehnica of Bucharest
Splaiul Independentei nr. 313,
Sector 6, Bucuresti, ROMANIA
dan.novischi@gmail.com
Adina M. Florea
University Politehnica of Bucharest
Splaiul Independentei nr. 313,
Sector 6, Bucuresti, ROMANIA
adina.florea@cs.pub.ro
ABSTRACT
In this paper we present the Ant Intelligent Robot (AIR), a
miniature mobile platform designed for swarm robotic research
and education. The proposed system has a modular and
distributed architecture that provides the necessary versatility,
robustness and user accessibility to enable the study of a broad
range of applications, while achieving a low cost. We also present
a comparison between AIR and other platforms that were recently
used in collective experiments.
Categories and Subject Descriptors
B.8.1 [PERFORMANCE AND RELIABILITY]: Reliability,
Testing, and Fault-Tolerance; I.2.9 [ARTIFICIAL
INTELLIGENCE]: Robotics – autonomous vehicles,
commercial robots and applications, sensors.
General Terms
Design, Performance, Reliability.
Keywords
Mobile robot platform, modular design, distributed hardware
architecture, small size robot, multi-robot systems.
1. INTRODUCTION
Multi-robot systems, as a subfield of multi-agent systems,
have attracted the attention of many researches in the past decade.
The attention received by these systems is twofold: hardware
advancements – a typical smart phone of today’s world is
equipped with a CPU running at several hundreds of MHz – and
the expected benefits of their improved efficiency – applications
in this respect include autonomous exploration, unmanned search
and rescue missions, dynamic target tracking, hazardous
environment monitoring, waste cleaning and others. In these
applications teams of robots not only have to rely on local
information and overcome the inherent problems with imperfect
sensors and communication medium, but they must intelligently
coordinate in order successfully accomplish the tasks at hand.
Moreover, the dynamic of various application environments, time
constraints, robustness, flexibility, cost, size, ease of use and the
ever increasing demands of user requirements add to the
complexity of designing such robot platforms.
In this paper we present the Ant Intelligent Robot (AIR), a
miniature mobile platform that provides the versatility, robustness
and flexibility necessary for the study of single- and multi-robot
applications, while still achieving a low cost. The modular and
distributed architecture of the platform meets key design goals
facilitating user accessibility – required in education – and multi-
agent system engineering – required in swarm robotic research. A
comparison between AIR and similar mobile robot platforms that
were recently used in collective experiments is also provided in
this paper.
2. RELATED WORK
To date research on swarm robotics has mainly focused on the
underlying multi-agent system design. As such, there is a large
body of work addressing key application areas: bio-inspired
systems [6], coordination [15], task-allocation [27],
communication [11], localization and mapping [30] and control
approach [9], just to name a few. Reviews on multi-agent research
areas for swarm robotics can be found in [19][37] and a more
recent review on swarm robotics multi-agent engineering can be
found in [28]. In order to support the requirements of these multi-
robot systems the individual robot designs are task centric
[5][7][13][16][25] or make tradeoffs between: cost, modularity,
size, processing power, energy consumption, versatility of sensory
systems and communication capabilities, robustness , ease of use
and monitoring [1-4][8][12-14][16][19-21][28][30].
Early work on mobile robot designs used in swarm robotics
research and education was done by Mondada et al. [1]. This
work featured Khepera, a small robot with only a 55 mm
diameter, but very limited processing power, flexibility and ease
of monitoring. This design was the basis for the subsequent
commercial versions: Khepera II, Khepera III and Khepera IV
developed by K-Team Co. [20][33]. The Khepera IV robot is
targeted toward research applications and costs $2000+ in parts
for the basic configuration, which may inhibit its use in swarm
robotic applications. A more versatile and also small size robot
design is Alice [3]. To the best of our knowledge, Alice, is the
smallest mobile robot to date, having a rectangular shape with a
side of only 20 mm. It also has a modular hardware design and
multiple robots can be collectively monitored through a radio link.
Several other mobile robot designs are relevant to our own: S-bot
[2], JL-I [7], ZeeRO [4], Kobot [12], Centibots [5], Bebot [20],
E-Puck [10][16][22]. These platforms have processing power
ranging from 20MHz – 400 MHz, provide range and local
.
BICT 2015, December 03-05, New York City, United States
Copyright © 2016 ICST
DOI 10.4108/eai.3-12-2015.2262874
communication capabilities using IR sensors, have wireless or
Bluetooth for long range communication and multi-robot
monitoring, are equipped with a vision sensor and have sizes
ranging from 120 mm to approximately 350mm in diameter. Some
of the designs also have modular hardware architecture which
may provide an increased robustness in the face of various
hardware failures.
More recent robot designs leverage key desirable features –
modularity, processing power, sensor variety communication
mechanisms and energy consumption – against cost and size.
MarXbot [21] is a design that features some improvements over
its predecessor the s-bot [2]. It has more processing power, more
energy – with its higher capacity battery and “hotswap” system –
and an increased bandwidth for inter-module communication.
These improvements are leveraged over the size of the robot –
being with 50 mm larger than the s-bot. The platform was used in
“Swarmanoid” project [25] and other related projects [23].
Flockbots [30] is a low cost design (approximately 500$) that
uses off-the-shelf components and is built around two of the most
popular embedded boards: Raspberry PI [31] and Arduino [38].
The sensory system is composed of a pan-tilt camera system and 5
IR sensors. In the extreme of low cost is Kilobot [24] with a
manufacturing price of only $14 in parts. The robot size is only 33
mm and it uses a slip-stick locomotion system based on vibration
motors. Communication and sensing is limited and is
accomplished through a single IR sensor. Wolfbot [28] is another
low cost robot design ($550 in parts) with an approximate
diameter of 178 mm that is built around the popular Beaglebone
embedded board [32]. The design incorporates an accelerometer,
a vision sensor with integrated microphone, 5 IR sensors and
wireless communication. Navigation with the Wolfbot is achieved
through an omni-directional drive system.
Finally, in designing the AIR platform we also considered self-
reconfigurable robot designs such as those presented in [17][19].
Design challenges for this type of systems are presented in [14]
and a review of architectures and existing platforms to date was
done by Yim, Mark et al. in [8].
3. DESIGN GOALS
In order to provide the robustness, flexibility and scalability
required by multi-robot research applications, as well as ease of
use, versatility and maintainability for use in education at a low
cost, we considered design aspects along three directions, namely:
multi-agent system engineering, educational requirements and the
manufacturing process.
3.1 Multi-agent System Engineering
In swarm robotic research applications, multi-agent system
engineering plays a crucial role, as it shapes key aspects of the
robotic swarm, such as: what are the underling physical
mechanisms used in self-coordination and task allocation, what
schemes of interaction and/or communication are required, what
are the characteristics of the environment in which robots will act
and many others. To support the research endeavors for these
systems in designing our platform we made the fowling
considerations:
1. The physical environment in which the robotic swarm will act
is: partially observable, stochastic, dynamic and continuous. This
assumption is based on the several key facts, namely: real sensors
and actuators present inherent imperfections, the state of the
environment may change both with and without the swarm acting
upon it and evolves over time according to laws of physics with
some degree of randomness.
2. Robots are multipurpose, have a variety of skills, are able to
interact using some form explicit and/or implicit (direct or indirect
[36][37]) communication and are not intentionally adversarial. In
order to support various tasks, such as: collaborative
manipulation, multi-robot localization and mapping, search and
rescue, robots within the swarm must possess a variety of
perceiving mechanisms and must be able to communicate to
achieve coordination.
3. Individual robot failures can be detected and communicated to
the swarm with a probability greater than zero. This is a very
desirable characteristic, as it directly influences the robustness of
the swarm. Thus, a state of the art robot design should include
mechanisms for detection and communication of sub-system
failures and execution of actions.
4. Robots within the swarm must be able to act in a real-time
manner according to the application specification in order to
successfully meet the application goals according to some
optimization criteria (e.g. lowest possible energy consumption,
achieve a certain goal in a given time, etc.). Thus, the platform
should be equipped with a considerable amount of processing
power in order to support processing intensive algorithms
employed by agents that run on the robots.
5. The individual robots must have a relatively small size in order
to be able to accommodate a large number of them in a relatively
limited space.
6. The design must support tools for multi-robot programming
and monitoring in order to facilitate the development of multi-
agent applications.
3.2 Educational Requirements
For educational purposes our robot design assumptions were
based on maximizing the students experience while providing a
broad range of engineering applications, namely:
1. The software tools and middleware for the robot platform
should provide a comprehensive and easily accessible interface
for the students. This feature is aimed at providing a self
contained programming environment and easy access to robots
hardware while imposing minimal limitations for use of other
software tools.
2. The platform should be versatile enough to provide a wide
range of educational applications, such as: signal and image
processing, embedded programming, automatic control,
distributed systems, sensor fusion, planning, machine learning,
etc…
3. The energy management of the design should maximize the
running time versus the off-time in order to increase the user
experience and the robot reliability.
3.3 Manufacturing Process
From the manufacturing stand point our design goals are based on
cost, ease of assembly and maintainability:
1. The design should be modular in order to facilitate easy
assembly and/or modification.
2. The composing modules must have a simple enough design in
order to allow easy repair and maintainability.
3. The design should be as low cost as possible in order to be
affordable even for single unit manufacturing. This also
guarantees low costs for repair or unit replacement.
4. AIR PLATFORM
The Ant Intelligent Robot (AIR) in Fig. 1 is a miniature platform
of rectangular shape with a side of 100 mm. The platform is
composed of several modules, namely: main processing unit,
range and local communication, vision, audio and navigation.
Each module represents a single independent unit that has its own
processing power, communication interface and power regulation.
This distributed architecture (see Fig. 2) is essentially different
from the classical single processing unit approach
[1][3][5][16][24][28][49] and has several advantages:
• It provides the ability to physically change the skill set of the
robots without the need to for hardware redesign by simply
adding or removing certain modules.
• Periodic and preemptive tasks necessary for signal
processing, motion control and many others can be
distributed among the composing modules reducing the
computational load for the main processing unit.
• It provides the ability to sense and communicate individual
module malfunctions and action execution faults improving
the robustness of the robotic swarm (see Software
subsection).
• It has a higher degree of flexibility for assembly and
maintenance related tasks.
Figure 1. Ant Intelligent Robot Platform.
Figure 2. Platform hardware architecture.
In the order to achieve a low cost in the implementation of our
platform we used both off the shelf and custom made components.
Inter-module communication is provided through a serial UART
connection (see fig 2). Although higher data rates can be achieved
through CAN-bus, which our platform supports, we used serial
communication in order to maintain flexibility in choosing off the
shelf components. Future extensions of our platform such as
adding a gripper is supported through GPIO, ADC, I2C, UART
and CAN busses.
The base of our robot (Fig. 3) is a low cost solution of only $4. It
is composed of six acrylic plastic parts corresponding to the sides
of the robot, interconnected through metallic angled brackets.
This base houses all components required by our design and
facilitates easy access to hardware components for repairing and
maintainability purposes.
Figure 3. Base isometric CAD drawing.
4.1 Main Board
The main board in Fig. 4 is a custom made printed circuit board
(PCB) that holds the main processing unit, the audio module and
the communication interfaces for the other modules and
extensions. It also provides the necessary circuitry for battery
management, signal conditioning and status LED’s.
Figure 4. Base isometric CAD drawing.
Table 1. Beaglebone vs. Raspberry Pi comparison
Feature Beaglebone Raspberry Pi
Proc. Type Arm Cortex-A8 ARM11
Proc. Speed 1GHz 700 MHz
RAM 512 MB DDR3L 512 MB SDRAM
GPIO 69 12
ADC 7 None
UART 5 1
CAN 1 None
I2C 2 2
USB 2 2
Ethernet 1 1
Min. Power 1.05W (210mA) 3.5W (700mA)
Cost $45 $35
For the main unit we used the Beaglebone [32] single board
computer (SBC) which comes in two versions. Both of these
versions are supported by our mother board. The choice of
Beaglebone over similar SBC’s such as the Raspberry Pi [31] was
based on its more powerful CPU (ARM Coretex-A8), lower
power consumption and available interfaces. Table I shows a
comparison between the two boards.
4.2 Perception
Perception capabilities of the Ant Intelligent Robot are distributed
among four modules, namely: range and local communication,
vision, audio and navigation. Each module maintains up-to-date
sensed information that is provided to the main processing unit on
a request basis.
For vision we used an off the shelf Linksprite camera [40]
equipped with a 90 degree lens that can capture 640x480 JPEG
encoded images at 30 fps. The audio module is an EasyVR [41]
voice recognition board. This board allows both speaker
dependent and independent commands to be trained. Thus, it
provides support for easy development of human-robot interaction
applications.
The range and local communication module is composed of a
custom standalone acquisition board (see Fig. 5), eight custom IR
sensors and two Maxbotix EZ0 [39] sonar range finders. The
acquisition board integrates the interface for managing both
sensor types and all signal filtering and post-processing
functionality. The IR sensors are mounted along the
circumference of the platform (two on each side), while the sonar
sensors are mounted in front. Our custom IR (see Fig. 5) sensors
have emitters and receivers that are modulated at 38 kHz to avoid
ambient light interference and feature two modes of operation:
active and passive. In active operation the sensors perceive
proximity of objects within 100 mm, while in passive operation
the sensors are used for local communication that is detailed later
in this section.
Figure 5. Range and local communication acquisition board –
left; IR sensor front and back side – right.
From the perception stand point, the navigation module provides
odometry sensing capabilities. These capabilities are discussed
next.
4.3 Navigation
AIR’s navigational module (see Fig 6) is composed of a
differential drive motion control board (MCB), a two stage H-
bridge and geared DC motors with magnetic quadrature encoders.
The module provides a command interface for controlling the
speed and position of the robot and includes capabilities such as:
traveling on a straight line with a specific speed, traveling on an
arc of specific length and radius at a certain speed, maintaining a
certain ratio between the two motor speed, rotating in place to a
specific angle and many others. The module also acts as a position
sensor for the main unit. The position is maintained in odometric
form (x, y, θ) and is updated at a rate of 1 ms by the control loop.
Figure 6. Motor control board: and back side.
In order to support applications where more torque is needed (e.g.
collaborative box pushing) versus application where more speed
is needed (e.g. collaborative target tracking) the geared motors we
selected have a maximum torque of 0.91 Nm and maximum speed
of 130 rmp at the gear output shaft. The motors are equipped with
low resolution magnetic encoders (only 48 counts per revolution)
in order to keep the costs low for our navigation solution. Due to
this choice of low resolution encoders, to accurately control the
differential drive, we designed a real-time closed-loop control
algorithm that runs at rate of 1 KHz. Figure 7 shows the
navigation block diagram for the control and odometry sensing
scheme.
Figure 7. Navigation block diagram.
The algorithm greatly improves the resolution with which the
speed is sensed through a sensor fusion scheme allowing us to
smoothly control the platform. It also minimizes the effects of
robot skid and slip through a cross-coupling method. Further
details about our control algorithm are presented in [42].
4.4 Communication
Communication – local and global – is of special interest to
swarm robotics because it is employed by a vast majority of
collective robot algorithms. It is also the underling mechanism
through which the behavior of the multi-robot system is
monitored and analyzed. Therefore, it is critical that the AIR
platform supports both local and global communication
mechanisms.
Multi-robot programming and monitoring are provided through
three interfaces, namely: USB, Ethernet and Wi-fi. The first two
are included by our Beaglebone main unit, while the last is
achieved by using an Edimax Wi-fi [43] dongle. The dongle is
mounted on the primary USB port of the Beaglebone. Wi-fi
communication is also used as a global communication
mechanism and is made available through our middleware
described in the Software subsection.
Local communication is achieved by using the IR sensors in
passive mode. When in this mode, the IR’s can sense the signals
emitted by other sensors. This mechanism equips AIR with
implicit (via sensing) communication abilities [44] and increases
the robustness of our platform in face of global communication
failures. Explicit communication through the IR sensors is also
possible, but it is reserved for future work at the time of this
writing.
4.5 Power Management
AIR’s energy consumption is distributed among its modules. At
full load (all modules are running, all sensors are enabled and the
motors run continuously at full speed) AIR draws a maximum
current of 0.697 A. In order to minimize the down time of the
platform we selected a three cell lithium polymer battery with a
capacity of 1.5 Ah and a 3 A high charge current rate. This
enables AIR to have over 2 hours of running time under
continuous full load and a charge time of only 30 minutes.
Charging a single unit is accomplished by using an off the shelf
tree cell balanced charger, while charging multiple units is
accomplished by using the same single unit charger and an off the
shelf parallel charge adaptor. This setup enables us to easily
charge up to six robots in the same amount of time that a single
unit would be charged.
4.6 Software
The goal of providing comprehensive software tools in order to
maximize AIR’s accessibility and facilitate easy application
development has been realized through our middleware. The
middleware is divided in three parts: operating system,
development environment and AIR’s standard development kit
(SDK). For the operating system, we use a custom real-time Linux
3.8 kernel with an Ubuntu 14.04 root file system which is
deployed on the Beaglebone. This setup provides a wealth of
software packages and libraries, such as OpenCV [46] or CMU
Sphinx [47], with minimal impact on the main unit processing
power. The development environment is based on Eclipse [45]
with additional plugins for monitoring (remote access, image
visualization) and a suite of cross-platform development tools
(compilers, linkers and debuggers). This environment can be
deployed on a wide range on Linux enabled machines and offers
an integrated development solution for users, as well as the ability
to easily deploy software on multiple targets and real-time
debugging capabilities over a Wi-fi connection. Both
development host and robot installation setups (either single or
multiple) are managed by custom automated shell scripts. As a
result, increasing the number of robots or changing the
development host is completely transparent. In order to facilitate
easy access to AIR’s hardware a C/C++ SDK was written. The
SDK is structured in five components: video, audio, range and
local communication, navigation and Wi-fi. The five components
are managed internally in a distributed manner by five threads of
execution, where each thread acts as a client for the five
components. The threads are responsible for providing up-to-date
information to the main unit and checking module fault condition
status. This scheme enables an agent running on AIR to
immediately sense both command execution and module failures.
It also enabled us to implement all of the user interface functions
for each module in a non-blocking manner. As a result, processing
intensive agent loops are not stalled due to hardware delays and
are able to have immediate access to the most recent precepts. To
illustrate, the following piece of code:
#include <iostream>
#include <Robot.h>
int main(int argc, char *argv[]){
Robot::init();
std::cout<< “x = ”<<Robot::motors-
>getX()
<<” , ”<< “y = “<< Robot::motors-
>getY()
<< std::endl;
Robot::close();
return 0;
}
displays the robot relative coordinates at the console:
$ x = 0 , y = 0
and represents an example of how a user would normally use the
platform SDK. In this example, the navigation module is accessed
through the motor object and the measured (x, y) relative
coordinates are requested. The reader is correct to assume that the
getX() and getY() methods return immediately, and that any
module on the AIR platform can be accessed in the same manner
as the navigation module (e.g. range and local communication
accessed through the sensors object). Thus, the requirements for a
user to start using the AIR platform are knowledge the of C\C++
programming language and a basic description of the sensory and
actuation system.
4.7 Cost
To allow manufacturing of large AIR collectives, one of our
design goals was to achieve a low price for single unit
manufacturing. When manufactured in single units AIR costs
$390. This cost is distributed among the parts of the composing
modules. Table II shows a summary of the overall costs for these
parts which include logistic costs. Dissemination of the data files
(hardware and software), building instructions and the list of
materials is acomplished through our group website [50].
Table 2. Parts costs
Part Cost
Beaglebone $45
Camera $45
Audio $50
Proximity and Range Sensors $60
Geared Motors with Encoders $60
Motor Driver $50
Custom Printed Circuit Boards $40
Miscellaneous Electronics $15
Battery $10
Mechanics $15
Total $390
5. PLATFORM COMPARISON
A wide range of mobile robots are available both commercially
and open-source. In this section we consider a subset of them that
we find relevant to our own design. This subset is presented in
Table III. Most of the platforms have a modular architecture, are
equipped vision, range and position perception capabilities, use a
differential drive for navigation and provide an interface for
developing software for the robots.
Commercially available robots, such as Khepera IV, Amigo or E-
Puk, feature a compact design and can be easily equipped with
additional extensions. However, the prices of these robots are in
range of thousands of us dollars in parts, which drastically limits
there use in swarm robotics applications. In comparison, AIR, has
a significantly lower price in parts, is equipped with more
processing power, features a fault-tolerant distributed design and
supports a integrated multi-robot development environment.
Similar – perception, processing, communication and ease of use
– capabilities are provided by the WolfBot platform. While this
platform has an increased autonomy provided by its larger battery
and a modular design, its size is with 78% larger than AIR and all
processing is done by the Beaglebone main unit. WolfBot also
relies on a less accurate navigation solution than AIR that is based
on an omni-directional drive and inertial measurements.
Diverging from the classical single processing unit approach is
ZeeRO with a clear modular and distributed design. Nonetheless,
AIR has significantly more processing power, smaller size and a
Table 3. Platform comparison
Robot Arch. Processing
Power Perception Navigation Comm. Autonomy Accessibility Size Cost in
parts Ref
Khepera
IV Modular ARM
Cortex-A8
800 MHz
Vision, Audio,
Range,
Proximity,
Inertial unit,
Position
Differential
Drive
Explicit-
global via
Bluetooth
& Wi-fi
5h
- Single-robot
IDE with USB
Bluetooth &
Wi-fi support
- C/C++ SDK
14 cm
Diamete
r $2000+ [33]
Amigo Non-
Modular SH2-7144
44 MHz Range,
Position Differential
Drive
Explicit-
global via
Wi-fi > 2h
- Single-robot
programming
via Wi-fi
- C/C++ SDK
33 x 28
x 15 cm $2000+ [5]
[49]
E-Puk Modular 30F6014A
64 MHz
Vision, Audio,
Proximity,
Inertial unit,
Position
Differential
Drive
Explicit-
global via
Bluetooth > 2h
- Single-robot
programming
via Bluetooth
- C SDK
7.5 cm $1000+ [16]
JL-I Semi-
distributed PXA255
400MHz
Vision,
Position,
Inertial unit
Differential
Drive
Explicit-
global via
Wi-fi 4h N/A 35 x 25
x 15 cm N/A [7]
ZeeRO Modular &
Distributed
PXA255
400MHz
Vision, Range,
Proximity,
Position
Differential
Drive
Implicit-
local via IR
sensing
Explicit-
global via
Bluetooh
N/A - Multi-robot
Server-Client
Interface
25 cm
diameter $600+ [4]
Kobot Modular &
Distributed 16F877A
20 MHz
Opt. Vision,
Proximity,
Position
Differential
Drive
- Implicit-
local via IR
sensing
- Explicit-
local via IR
7.5h - Multi-robot
programming
via Wi-fi
12 cm
diameter $650+ [12]
BeBot Modular &
Distributed
ARM
Cortex-A8
600 MHz
Vision, Range,
Position,
Inertial unit
Differential
Drive
Explicit-
global via
Wi-fi or
Bluetooth
N/A
- Single-robot
IDE with
RS232,
Bluetooth &
Wi-fi
- C/C++ SDK
9 cm
sides N/A [19]
MarXbot Modular &
Distributed ARM11
533 MHz
Omni-vision,
Range scanner,
Proximity,
Position,
Audio, Inertial
unit
Differential
Drive
- Implicit-
local via IR
sensing
- Explicit-
global via
Wi-fi
4h with
hotswap
system
- Multi-Robot
IDE
- Custom
language SDK
(ASEBA)
17 cm
diameter N/A [21]
Flockbot Non-
Modular ARM11
700 MHz Video, Range,
Position Differential
Drive
Explicit-
global via
Wi-fi 2h
- Single-robot
programming
via Wi-fi
- C/C++ SDK
18 cm
diameter $500 [30]
WolfBot Modular ARM
Cortex-A8
1 GHz
Video, Audio,
Range, Light,
Inertial unit
Omni-
directional
drive
- Implicit-
local via IR
sensing
- Explicit-
global via
ZigBee
6h
- Multi-robot
programming
via Wi-fi
- Python SDK
17.8 cm
diameter $550 [28]
AIR
Modular,
Distributed
& Fault-
tolerant
ARM
Cortex-A8
1 GHz
Video, Audio,
Range,
Proximity,
Position
Differential
Drive
- Implicit-
local via IR
sensing
- Explicit-
global via
Wi-Fi
> 2h
- Multi-robot
IDE with USB,
Ethernet &
Wi-fi support
- C/C++ SDK
10 cm
sides $390
lower cost. On the other hand, MarXbot, the predecessor of s-bot,
has more perception capabilities than AIR. It also has provides
self-assembly capabilities through a custom designed gripper and
increased autonomy through a hotswap system. Both s-bot and
MarXbot are larger in size and have with more than 50% less
processing power. A more compact design is BeBot, with 10%
smaller in size than AIR. However, AIR has both explicit and
implicit communication capabilities and more processing power at
its disposal.
Other platforms such as the JL-I, Kobot or Flockbot, in
comparison with AIR, achieve higher costs with less processing
power at an increased size. AIR, also has a more accessible user
interface, offloads the main unit by distributing processing tasks
to composing modules and provides fault-tolerant capabilities
through fault condition status checking for each of its
components.
6. CONCLUSION
In this paper we presented the Ant Intelligent Robot (AIR), a
miniature mobile robot platform designed for swarm robotic
research and education. AIR’s modular and distributed
architecture is essentially different from the single processing unit
approach and provides key features required by both research and
educational applications: versatility of perception and
communication mechanisms, accurate motion estimation and
navigation, increased fault-tolerance and processing power
capabilities, ease of use and multi-robot software development
tools support. Furthermore, it facilitates easy assembly and
maintenance while achieving a low cost.
A comparison with similar robot platforms was also presented.
The comparison includes several design characteristics, namely:
modularity and distributivity of the robot platform architectures,
processing power, perception and communication mechanisms,
autonomy, size, cost and user accessibility.
While AIR’s low cost and small size enable larger collectives of
robots to be built, its versatility and user accessibility allow a
board range of applications to be studied.
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