=Paper=
{{Paper
|id=Vol-1484/paper18
|storemode=property
|title=Adapting Low-Cost Platforms for Robotics Research
|pdfUrl=https://ceur-ws.org/Vol-1484/paper18.pdf
|volume=Vol-1484
|dblpUrl=https://dblp.org/rec/conf/iros/GeorgeCZJGW15
}}
==Adapting Low-Cost Platforms for Robotics Research==
https://ceur-ws.org/Vol-1484/paper18.pdf
Adapting Low-Cost Platforms for Robotics Research
Karimpanal T.G., Chamambaz M., Li W.Z., Jeruzalski T., Gupta A., Wilhelm E.
Abstract— Validation of robotics theory on real-world hard- available here:
ware platforms is important to prove the practical feasibility https://github.com/SUTDMEC/EvoBot Downloads.git
of algorithms. This paper discusses some of the lessons learned • Adaptable: The final platform is intended to be as
while adapting the EvoBot, a low-cost robotics platform that
we designed and prototyped, for research in diverse areas general purpose as possible, with minimum changes
in robotics. The EvoBot platform was designed to be a low- needed to be made to the base firmware by users in
cost, open source, general purpose robotics platform intended order to scale to a wide variety of common research
to enable testing and validation of algorithms from a wide applications. Some representative applications are de-
variety of sub-fields of robotics. Throughout the paper, we scribed in section IV. . . .
outline and discuss some common failures, practical limitations
and inconsistencies between theory and practice that one may In the process of designing the EvoBots platform, a
encounter while adapting such low-cost platforms for robotics great deal of failure-based learning was involved, and this
research. We demonstrate these aspects through four represen- paper compiles and synthesizes this process in order for it
tative common robotics tasks- localization, real-time control, to be useful for future robotics researchers who intend to
swarm consensus and path planning applications, performed
using the EvoBots. We also propose some potential solutions to
develop their own platforms, or adapt commercially available
the encountered problems and try to generalize them. platforms. Specific emphasis will be on challenges common
Index Terms — low cost, open-source, swarm robotics, lessons to robotic platforms in general, and approaches used to avoid
learned failure when attempting to solve them. The authors would
like to point out that each robotics project presents a unique
I. I NTRODUCTION sets of problems, so we have attempted to only describe
problems we think may be generalized.
The Motion, Energy and Control (MEC) Lab at the
Singapore University of Technology and Design (SUTD) is II. P RECEDENTS AND E VO B OT D ESIGN
engaged in an ambitious research project to create a swarm This section will discuss commonalities and differences
of autonomous intelligence-gathering robots for indoor en- between the EvoBot and other comparable low-cost robotics
vironments. After reviewing commercially available low- platforms, as well as provide insights into the lessons learned
cost robotics platforms, some of which are shown in Table during the design process. In order to reduce the time be-
I, the decision was taken to build a custom ground-based tween design cycles, the EvoBots were prototyped with a 3D
robot platform for performing swarming research, dubbed printed body and developed across three major generations
the ’EvoBot’. The primary trade-off is between platform and several minor revisions. An exploded view of the EvoBot
flexibility and cost, where existing robots are intended to is shown in Figure 1.
either be applied in specific research areas, and are hence Like the Khepera, Finch, Amigobot (table I) and most of
equipped with limited and specific sensor and communica- the other platforms, locomotion on the EvoBot is achieved
tion capabilities, or are more flexible with respect to sensor
and firmware packages, but are also more expensive. In
terms of software control development environments, well-
established frameworks such as ROS [1] or the Robotics
Toolbox [9] have extremely useful modular functions for
performing baseline robotics tasks, but require substantial
modification between applications and require specific oper-
ating systems and release versions.
With this in mind, the MEC Lab set out to develop the
EvoBot platform with the following goals:
• Low cost: affordable for research groups requiring a
large number of swarming robots (e.g. more than 50)
with sufficient sensing and control features.
• Open source: The hardware, body/chassis design, appli-
cation software and firmware for the EvoBots are fully
open source in order to enable any group to replicate Fig. 1. The 3-D printed case has two slots at the bottom for the optical
flow sensors, a housing for the left and right tread encoders, and 5 IR depth
the platform with minimal effort. All the hardware and sensors. The encoders on the forward wheels and the optional ultrasonic
software files used for the design of the EvoBots is sensors are not shown
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The path to success: Failures in Real Robots October 2, 2015
TABLE I
high even after using methods such as the Kalman filter [15].
P RECEDENTS FOR LOW- COST RESEARCH ROBOTICS PLATFORMS
Although obtaining the position estimates from the IMU is
Platform Est. Cost (USD) Commercial Scholar Hits Schematics Code
not recommended in general, good estimates can be obtained
Khepera [19] 2200 Yes >1000 No Yes with units that are capable of a much higher resolution and
Kilobot [22] 100+ Yes 82 No Yes data rate. Such units however, come at an expense and thus
e-Puck [18] 340+ Yes >1000 Yes Yes may not be the ideal choice for a low-cost platform.
Jasmine [4] 150+ No >1000 Yes Yes
Formica [11] 50 No 10 Yes Yes A. Sensing Features
Wolfbot [6] 550+ No 4 Yes Yes While the sensors mentioned earlier in this section focused
Colias [3] 50+ No >1000 No No on features shared with other platforms, there are some
Finch [17] 100 Yes 73 No Yes sensing, control and communication features specific to the
Amigobot [2] 2500 Yes 170 No Yes EvoBots.
EvoBot 300 Yes 0 Yes Yes In almost all robotics applications, errors in the robot’s
position estimates may gradually accumulate (e.g. for ground
robots, skidding or slippage of the wheels on the ground
surface confound the wheel encoders). In the first two gener-
using a differential drive system, with motors on either side ations of the EvoBots, this error in the wheel encoders caused
of its chassis. Although this system introduces kinematic substantial challenges for localization. In order to tackle this
constraints by restricting sideways motion, the associated problem, optical flow sensors were placed on the underside of
simplicity in manufacturing and assembly, and in the mathe- the EvoBot. They detect a change in the position of the robot
matical model for use in control applications are significant by sensing the relative motion of the robot body with respect
advantages. The wheels are coupled to the motors through a to the ground surface. This ensures that the localization can
gearbox with a gear reduction ratio of 1:100. After reduction, be performed more reliably even in the case of slippage. As
the final speed of the robot can be varied from -180 to shown in Figure 1, there are two such sensors placed side
180 mm/s, so that both forward and backward motions are by side at a specific distance apart. This arrangement allows
possible. inference of heading information of the robot along with
The speed of each motor is controlled by a pulse width information of the distance traveled. Although the optical
modulated voltage signal. In order to ensure predictable flow sensors perform well in a given environment, they need
motion, an internal PI controller is implemented by taking to be calibrated extensively through empirical means. In
feedback from encoders that track the wheel movement. addition, it was found that the calibration procedure had to
The controller parameters may need to be hand-tuned to be repeated each time a new surface is encountered, as the
compensate for minor mechanical differences between the associated parameters are likely to change from surface to
two sides of the robot, arising from imperfect fabrication surface.
and assembly. As described above, the wheel encoders, optical flow as
For obstacle detection and mapping applications, 5 infra- well as the IMU sensors can all be used to estimate the
red (IR) sensors were placed on the sides of a regular robot’s position and heading. Each of these becomes relevant
pentagon to ensure maximum coverage, with one IR sensor in certain specific situations. For example, when the robot
facing the forward direction. Similar arrangement of range has been lifted off the ground, the IMU provides the most
sensors is found in platforms such as Colias and e-puck reliable estimate of orientation; when the robot is on the
(table I). Despite this arrangement, there exist blind spots ground and there is slippage, the optical flow estimates are
between adjacent sensors, which could lead to obstacles not the most reliable; and when there is no slippage, the position
being detected in certain orientations of the robot relative estimates from the encoders are reliable. Some details on
to an obstacle. The use of ultrasonic sensors instead of the use of multiple sensors for localization are mentioned in
IRs, could reduce the probability of occurrence of blind section IV-A.
spots due to their larger range and coverage, but they are In their current configuration, the EvoBots also include
also more expensive. It is thus advisable to choose sensors the AI-ball, a miniature wifi-enabled video camera with
appropriately and to thoroughly test their performance and comprehensive driver support and a low cost point to capture
limitations before a final decision on the physical design and video and image data [25]. The camera unit is independent
sensor placements is made. of the rest of the hardware and thus can be removed without
Another feature present in the EvoBots common to other any inconvenience if it is not needed.
platforms is the use an inertial motion unit (IMU). The
6DOF IMU gives information regarding the acceleration of B. Control and Communication Features
the robot in the x, y and z directions, along with roll, There are a wide variety of microcontrollers which are
pitch and yaw (heading) information. Although in theory, presently available on the market. The chipset used on the
the position of the robot can be inferred using the IMU data, EvoBot is the Cortex M0 processor, which has a number
in practice, these sensors are very noisy, and the errors in of favorable characteristics such as low power, high per-
the position estimates from these sensors are unacceptably formance and at the same time, is cost efficient, with a
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The path to success: Failures in Real Robots October 2, 2015
large number of available I/O (input/output) pins. One of the A. Sensing
primary advantages of this processor is that it is compatible While it may be considered acceptable to use ideal sensor
with the mbed platform (https://developer.mbed.org/), which models in simulation, in real-world robotics applications,
is a convenient, online software development platform for and especially in the case of low-cost platforms, sensing
ARM Cortex-M microcontrollers. The mbed development information about the environment can be done only with
platform has built-in libraries for the drivers, for the motor a certain degree of range and accuracy. It is thus important
controller, Bluetooth module and the other sensors. to be judicious in choosing the location of the sensors. For
Like the e-puck, the EvoBots have Bluetooth communica- the case of range sensors for obstacle detection, blind spots
tion capability. After experimenting with various peer-to-peer must be avoided as much as possible to prevent unexpected
and mesh architectures, it was determined that in order to behaviours. Also, the upper and lower limits of the sensor
maintain flexibility with respect to a large variety of potential range must be considered. As in the case of the EvoBots
target applications, having the EvoBots communicate to a or other low-cost platforms, the sensor range may be quite
central server via standard Bluetooth in a star network limited and prone to noise, and blind spots exist despite
topology using the low-cost HC-06 Bluetooth module was careful sensor placement. In case of occurrence of blind
the best solution. Sensor data from the various sensors spots, it may be worth encoding default behaviours into
gathered during motion is transmitted to the central server the robot such as conditional wall following or automatic
every 70ms. The camera module operates in parallel and uses steering away from detected obstacles.
Wi-Fi 802.11b to transfer the video feed to the central server.
The Bluetooth star network is also used to issue control B. Calibration
commands to the robots. The behaviour of sensors are typically considered to be
In addition, having a star network topology with a central same for different environments during simulation. In prac-
server indirectly allows additional flexibility in terms of tice, most sensors need some form of calibration. In addition,
programming languages. Only a Bluetooth link is required, the calibration parameters for certain sensors may depend on
and all the computation can be done on the central server. the environment in which they operate. For example, the
For example, the EvoBot can be controlled using several calibration parameters for the optical flow sensors in the
programming languages, including (but not limited to) C, EvoBots vary based on the surface on which they operate. In
python and M AT LAB. The Bluetooth link also opens up addition, the parameters are very sensitive to small variations
the possibility of developing smartphone mobile applications in ground clearance which can be caused by variations in the
for it. Table II summarizes all the sensors used in the EvoBot. robot body due to imperfect 3D printing or during assembly.
It is often the case that parameters will have to be set
TABLE II and reset from time to time. This can considerably add
T HE FINAL SENSING CAPABILITIES OF THE 3 RD GENERATION E VO B OT to development time and effort. Automating the calibration
PLATFORM process should be done whenever possible. If automation is
not possible, one way to mitigate this issue is to have the
software package include user commands that can change
Signal Sensor Frequency Full Range Accuracy
calibration parameters on the fly. This was the solution
acceleration MPU6050 1000 Hz +/- 0.1% adopted for the EvoBots in order to calibrate the encoders
Temperature MPU6050 max 40 Hz +/-1 ◦ C from -40 to 85 ◦ C and optical flow sensors.
x/y pixels ADNS5090 1000 Hz +/- 5mm/m
C. Timing
Encoders GP2S60 400 Hz +/- 5mm/m
Battery current ACS712 33 Hz +/- 1.5 % @ 25C In simulation, the robot’s update loop and solvers can
Proximity GP2Y0A02YK0F 25 Hz +/- 1mm
have either fixed or variable time steps, but it is most likely
Camera AI-ball 30fps VGA 640x480
to be variable in practice. Thus, keeping track of time is
critical, especially for real-time applications. For example, in
the EvoBots, the performance of the localization algorithm
(section IV-A) significantly depreciates if the time step is
considered fixed.
III. G ENERAL P ROBLEMS
D. Communication
The EvoBot platform was designed to be useful for a When a large amount of sensor data and instructions
wide range of research problems, and to enable researchers needs to be exchanged between platforms or between a
working in theoretical robotics to easily shift their algorithms platform and a central machine, maintaining the integrity
from simulation to hardware. Moving from virtual to real of the communicated data is critical. When the exchanged
platforms introduces a set of additional issues which must information is delayed or lost either partially or completely
be considered. Some of these, which were encountered while during information exchange, it could lead to a series of
developing applications for the EvoBots, are discussed in this unwanted situations such as improper formatting of data,
section. lack of synchronization, incorrect data etc., These aspects are
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The path to success: Failures in Real Robots October 2, 2015
seldom accounted for in simulation. In the EvoBots, the issue 1
0.8
of lack of data integrity was tackled by performing cyclic 0.6 Robot 2
redundancy checks (CRC) [21] as part of the communication 0.4
routine. It is ideal to perform bit-wise checks as well, but this 0.2 Robot 1
Y (m)
0
has not been done in the case of the EvoBots. −0.2
The issues listed in this section apply to almost all robotics −0.4
Ground Truth
applications in general. The following section discusses some −0.6
Estimated (Kalman Filter)
−0.8 Just Model
of the applications and the associated issues encountered −1
Measurment Only
−1 −0.5 0 0.5 1 1.5 2
during their implementation using the EvoBots. X (m)
IV. A PPLICATIONS Fig. 2. The Kalman Filtering process improves the state estimate beyond
what the model and the measurements are capable of on their own.
This section discusses application-specific issues in diverse
sub-fields of robotics such as real-time control, swarm and
artificial intelligence applications. These applications were
selected because they are commonly implemented tasks from in the estimated state.One solution to this problem is to label
a wide variety of sub-fields in robotics. each sensor data package with time, compute the sampling
time on the central computer and use the computed real-time
A. Localization sampling time in the extended Kalman filter formulation.
Ground robots typically need to have an estimate of With these factors in mind, labeling the sensor data package
position and heading. The EvoBot platform, as well as many with time seems to be an efficient solution to deal with
other robots, are designed to be used in an indoor environ- variable sampling time.
ment, and therefore it is not possible to use GPS without A common problem associated with estimating the state
modifying the environment in question; also, positioning using the velocity sensors is “slippage”. In cases where
information provided by GPS has the accuracy in the order a robot slips on the floor, the wheel encoders reflect an
of a 101 meters which is not suitable for small ground robots. erroneous result. In the worst case, when the robot is stuck,
For this reason, the set of on-board sensors are used to the encoders keep providing ticks as if the robot is moving,
estimate the robot’s states of position and heading. The on- which leads to a significant error in the estimated position.
board sensors however, are typically associated with some To solve this, we utilized the information from the optical
noise and retrieving positioning information from them leads flow sensors and designed an adaptive Kalman filter. The
to erroneous results. For instance, integrating acceleration velocities reflected by the encoders and optical flow sensors
data to get the velocity and position does not provide accurate are compared and in case there is a significant difference
state estimation and leads to huge offsets from the true between them, the occurrence of slippage is inferred. Then,
value due to the associated noise. To overcome this issue, the noise covariance matrix in the extended Kalman filter is
state estimation is performed using a method based on the changed so that the filter relies more on the velocity provided
Extended Kalman Filter [15]. by the optical flow sensor. Thus, we infer that although
We used the information provided by the wheel encoders, having multiple sensors sensing the same quantity may seem
optical flow sensors and the heading provided by IMU’s redundant, each of them, or a combination of them may be
gyroscope to estimate the position and heading. The sensor useful in different contexts.
information is combined with the mathematical model of
the robot to estimate the position and heading of the robot.
Figure 2 presents the result of an experiment where two
robots follow a trajectory (red line) and the data from the B. Application in Real-time Control
sensors are used in the extended Kalman filter to estimate its
position and heading. In the next two paragraphs we explain In a perfect scenario where there is no disturbance and
two problems we faced while implementing the designed model mismatch, it is possible to use some feed-forward
Kalman filter and we describe the corresponding solutions control to drive the robot on a desired trajectory. However, in
used in our platform. the real world, a number of issues such as model mismatch,
Timing is one of the most important factors for localization disturbance and error in the internal state deviates the robot
and control tasks. The delay in communication between from its desired path. Therefore, feedback control is vital to
the robot and central computer where the Kalman filter is achieve an accurate tracking behavior. In general, the navi-
running, causes some variation in the time intervals within gation problem can be devised into three categories: tracking
which each sensor data package is received. For example, the a reference trajectory, following a reference path and point
time intervals can vary between 50 to 100 ms Therefore, as stabilization. The difference between trajectory tracking and
mentioned in section III, the sampling time in the extended path following is that in the former, the trajectory is defined
Kalman filter cannot be fixed a-priori. The state estimation over time while in the latter, there is no timing law assigned
procedure highly depends on the corresponding sampling to it. In this section, we focus on designing a trajectory
time and using a fixed sampling time leads to a large error tracking controller.
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The path to success: Failures in Real Robots October 2, 2015
The kinematic model of our platform is given by: 2
Reference Trajectory
Current Position
1.5
v1 + v2
x˙c = cos θc
2 1
y (meter)
v1 + v2 0.5
y˙c = sin θc
2 0
1
θ˙c = (v1 − v2 ) (1) −0.5
l
−1
−1.5 −1 −0.5 0 0.5 1 1.5
where v1 and v2 are velocities of right and left wheels, x (meter)
θc is the heading (counter clockwise) and l is the distance
between two wheels. As stated earlier, one of the reasons Fig. 3. The trajectory of the robot
for adopting a differential drive configuration for the EvoBot
is the simplicity of the kinematic model (1). Hereafter, we 2
Error in x
Error in y
use two postures, namely, the “reference posture” pr = 1.5
Errrot in θ
(xr , yr , θr )0 and the “current posture” pc = (xc , yc , θc )0 . The
1
error posture is defined as the difference between reference
posture and current posture in a rotated coordinate where 0.5
(xc , yc ) is the origin and the new X axis is the direction of 0
θc . −0.5
xe cos θc sin θc 0 −1
0 50 100 150 200 250
pe = ye = − sin θc cos θc 0 (pr − pc ). Time (sec)
θe 0 0 1
Fig. 4. The errors in x, y and θ
(2)
The goal in tracking control is to reduce error to zero as
fast as possible considering physical constraints such as One of the important factors in implementing the real time
maximum velocity and acceleration of the physical system. controller is time synchronization. The controller needs to
The input to the system is the reference posture pr and keep track of time in order to generate the correct reference
reference velocities (vr , wr )0 while the output is the current point. This issue is critical especially when a number of
posture pr . A controller is designed using Lyapunov theory robots need to be controlled. In this scenario, an independent
[16] to converge the error posture to zero. It is not difficult counter can be assigned to each robot to keep track of its
to verify that by choosing time and generate the correct reference point at each time
( step.
v1 = vr cos θe + kx xe + 2l (wr + vr (ky ye + kθ sin θe ))
C. Application in Swarm Robotics
v2 = vr cos θe + kx xe − 2l (wr + vr (ky ye + kθ sin θe ))
(3) The overarching application of the EvoBots platform is
the resulting closed-loop system is asymptotically stable for to enable low-cost swarm robotics research. The study of
any combination of parameters kx > 0, ky > 0 and kθ > 0.1 emergent collective behaviour arising from interactions be-
The tuning parameters highly affect the performance of the tween a large number of agents and their environment is an
closed loop system in terms of convergence time and the area which is gaining increasing importance recently. The
level of control input applied to the system. Hence, we chose robots used for these purposes are usually simple and large
parameters kx , ky and kθ based on the physical constraints in number, with communication capabilities built into them.
of our platform e.g. maximum velocity and acceleration. Although it is ideal to have peer-to-peer communication
Extensive simulation is performed to verify the controller (3). between the agents, the same effect can be simulated through
Figure 3 shows some experimental results where the robot a star-shaped network, where all the agents share information
follows a reference trajectory (blue curve). The reference with a central machine.
trajectory is a circle of radius 1m. The initial posture is A typical swarm robotics application is to have the swarm
selected to be (0, 0, 0)0 . Errors xr − xc , yr − yc , and θr − θc arrive at a consensus about some common quantity, and
are shown in Figure 4. We remark that reference linear to use this as a basis to collectively make decisions about
and angular velocities vr and wr are difficult to obtain for the next actions of each member [5]. Figure 5 shows the
arbitrary trajectories. We solved this problem using some configuration of a set of 6 robots at different times. The
numerical methods where the point-wise linear and angular heading/orientation of each robot depends on those of the
velocities are computed numerically. other robots and is governed by the update equation:
1 Choosing V = 1 (x2 + y 2 ) + (1 − cos θ ) and taking its derivative
2 e e e θi ← θi + K(θm − θi ) (4)
confirms that V̇ ≤ 0. Hence, V is indeed a Lyapunov function for the
system (1). where
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The path to success: Failures in Real Robots October 2, 2015
Fig. 6. Planned path of the robot shown in blue
designed to enable this objective falls under the fields of ma-
chine learning and artificial intelligence (AI). This includes
approaches such as neural networks [14], support vector
machines [10], evolutionary robotics [20], reinforcement
learning [23], probabilistic methods [24], etc., which are used
for a range of tasks such as object recognition, clustering,
path planning, optimal control, localization and mapping etc.
Although most algorithms can be tested out in a simulated
environment, the uncertainties and nuances associated with a
real environment limit the utility of simulated solutions, es-
Fig. 5. Overhead view of the robots at different times during the heading pecially in fields such as evolutionary robotics [7], [8], [12].
consensus. The robots are initially unaligned, but arrive at a consensus on
heading at t=10s A more thorough validation necessitates implementation of
these algorithms on real-world physical systems.
To illustrate some of the primary issues and requirements
of a platform for use in AI, a path planning task is demon-
X strated using the standard A-star algorithm [13]. The scenario
θm = θi /N (5)
involves planning a path from the starting position of a
Here, θi is the heading of an individual agent, K is a constant robot to a target position using a previously constructed
and θm is the mean heading of all N agents. These updates infra-red sensor based map of the environment as shown in
are performed on each agent of the swarm till the heading Figure 6. Most of the arena shown in Figure 6 has been
converges to the same value. mapped; the mapped areas are shown as black patches along
the boundaries of the arena. The planned path (shown in
As seen from Figure 5, the robots eventually orient them-
blue) is generated by the A-star algorithm, using cost and
selves in a direction that was determined using the consensus
heuristic functions. The heuristic function is proportional to
algorithm.
the euclidean distance from the target position. Some of the
Swarm algorithms rely on the current information
important issues while implementing this algorithm for path
regarding other members. So, delays in communication
planning and for AI applications in general, are listed below:
can cause swarming algorithms to diverge instead of
Computing Resources: The A-star algorithm could have
converging. As mentioned in section III, performing
also been implemented locally on the robots. However, this
routines to maintain the integrity of the exchanged data
would have implied storing a copy of the IR map and
is critical. The communication rate should ideally be
performing the A-star algorithm locally using this map. This
independent of the number of agents involved. Usually, in
may not have been feasible due to the limited on-board
communication systems, it is common practice to make use
memory and computational complexity of the algorithm.
of communication data buffers. For swarm applications,
Also, for path planning, the map of the environment gets
one should ensure the latest data is picked out either by
larger as the space explored gets larger. Accordingly, the
matching similarity in time-stamps or by refreshing the
A-star path planning could potentially become more com-
buffer before each agent is queried.
putationally intensive. So setting a resolution parameter (or
deciding to have one) is recommended before starting the
mapping.
D. Application in Artificial Intelligence
In general, sufficient computing resources will be needed to
One of the long-term goals of robotics is to develop carry out AI algorithms. Having a thin client system that
systems that are capable of adapting to unknown and unstruc- offloads the computational effort to a central server helps
tured environments autonomously. The general set of tools deal with the high computational costs imposed due to high
FinE-R 2015 Page 25 IROS 2015, Hamburg - Germany
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FinE-R 2015 Page 26 IROS 2015, Hamburg - Germany
The path to success: Failures in Real Robots October 2, 2015