Outstanding Innovation Award
QuantumSpeed

Line league · Italy · RoboCup 2026
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Poster1 pagePublished
Presentation videoYouTubePublished
Bill of materials1 pagePublished
Team description paper543 KBPublished
Engineering journalNot shared
Source code7.9 MB · GitHubPublished
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In their words

The robot is designed to compete in the RoboCupJunior Rescue Line. We have been working for almost three years to create a robot capable of overcoming all the challenges presented by the competition. Over time, the robot has undergone numerous modifications: we have added artificial intelligence systems; we have increased the number of sensors; we have used better and more suitable sensors for our goals and we have furthermore improved our mechanical components by the addition of a brand new tire system built from scratch. Thanks to these improvements, the robot has significantly enhanced its performance, demonstrating greater reliability in handling complex line-following sections and improved capability in overcoming physically challenging elements of the course. The entire robot was designed in 3D using Fusion 360.
To achieve greater reliability, we decided to rely only on one microcontroller, getting rid of the old serial communication system. In fact, all the computations are done by a Raspberry Pi 5, programmed in Python, connected to a Pi camera, which is used to identify and follow the line, using the tools provided by the OpenCV library, additionally the camera is used to manage the evacuation zone, recognizing the entrance and the exit of it. The Raspberry is also connected to an AI hat, using a model trained with TensorFlow Lite, it is able to recognize victims and evacuation points in the evacuation zone using a second vertical Pi camera.
Raspberry Pi 5 Python OpenCV TensorFlow Lite yolo26n PID algorithm Fusion 360 I2C Multiprocessing 3D print
Poster

Read the text of this document — 1395 words
SOFTWARE                                                                                                                                                                        TEAM
                                                                                      Line-following                              Evacuation Zone

                                                                                                                                                                                                                                                      The team was founded in 2023 under the original name 'Unicorno colorato'
                                                                                                                                                                                                                                                      (which translates to 'Colored Unicorn'). The initial team members were: Simone
                                                                                                                                                                                                                                                      Bettega, Matteo Defant, Samuele Contessotto, and Luca Facchinelli. Over the
                                                                         To follow the line, we implement a PID            For object recognition inside the evacuation
                                                                                                                                                                                                                                                      years, the team underwent several lineup changes (some team members and
                                                                         algorithm, a negative feedback system             room, we implemented an integrated                                                                                         friends can be seen in the 'nazionali 2025' image). In 2024, QuantumSpeed was
                                                                         widely used in automatic control systems.         computer vision pipeline. Sphere detection                                                                                 established with the goal of breaking into the national top 10. That year, we
                                                                         We detect the black line using an HSV             is performed using a TensorFlow Lite                                                                                       achieved a second-place ﬁnish, qualifying for the European Championship in
                                                                         threshold and calculate its center of mass.       machine learning model, trained in Google                                                                                  Bari (IT), where we reached the podium again in second place. This year, we
                                                                         Next, we determine its distance from the          Colab on a dataset of approximately 3,000                                                                                  decided to completely overhaul the robot's underlying technology, transitioning
                                                                         camera's center. Based on the difference          images. Triangle recognition, on the other                                                                                 from traditional sensors to a camera-based system. This choice led us to win the
                                                                         along the x-axis, the PID function calculates     hand, relies on an HSV mask–based                                                                                          regional competition, secure second place at the national level, and qualify for
                                                                         the required velocity variation for the wheels.   algorithm developed with the OpenCV                                                                                        the World Championship in Corea, which will mark our ﬁnal chapter in the RCJ.
                                                                                                                           library. Image acquisition is handled by the
                                                                         This variation is then multiplied using the                                                                                                                                                    Simone Bettega
                                                                                                                           Pi Camera Module 3 Wide, which provides a
                                                                         difference along the y-axis (see image); this                                                                                                                                                  AI model trainer and sistemistic expert
                                                                                                                           120° ﬁeld of view and real-time autofocus
                                                                         approach allows us to follow the line                                                                                                                                                          He developed and implemented a machine learning
At the beginning of this year, we decided to switch from sensors to                                                        capabilities.     The    entire    system     is
                                                                         smoothly and reliably.                                                                                                                                                                         system for silver line detection. He also implemented the
a camera setup. To do this, we introduced a Raspberry Pi 5 8GB                                                             programmed          in   Python,     and     its
                                                                         We also detect other colors using HSV             performance is improved by the AI HAT+                                                                                                       lack-of-progress recovery procedure using ﬁle service. In
equipped with two Pi Camera 3 Wide lenses. We chose the
Raspberry Pi because it is highly versatile, user-friendly, and          thresholds. When red is detected, the robot       accelerator delivering up to 13 TOPS of
                                                                                                                                                                                            1st Place                                                                   addition, he collaborated on improving the PID control
                                                                                                                                                                              Qualifying Tournament - Trento 2026
well-suited for a wide range of applications. Furthermore, we            stops instantly, while green tells it which       computing power, enabling inference                                                                                                          system, contributing to more accurate and stable robot
integrated a hardware accelerator to achieve even higher                 direction to turn. To recognize intersections,    speeds of around 30 FPS and ensuring                                                                                                         navigation. He also took care of creating the venv.
performance.The Raspberry Pi handles both line-following and             we also utilize concentric circles: if they       highly     reliable    and    efﬁcient   object
victim recognition. To run the entire system seamlessly, we set up       intersect the line more than twice, an            recognition. Finally, a specially designed                                                                                                   Luca Facchinelli
a dedicated environment and conﬁgured service ﬁles, allowing the         intersection is detected. Finally, to recognize   collection chest allows the robot to gather all                                                                                              Co - Captain, Line following programmer
code to execute autonomously.                                            the silver line, we employ machine learning       three spheres simultaneously, signiﬁcantly                                                        2nd Place                                  Luca has been part of the team since the beginning.
                                                                         and calculate the difference in reﬂected light    reducing the overall evacuation time and                         nd
                                                                                                                                                                                            2 Place                 European Robocup - Bari 2025                        Initially, his role was to coordinate the team, leveraging his
                                                                         with the LEDs turned on and off.                  improving task efﬁciency.                              Italian Open - Catania 2026                                                           prior experience in RoboCup Rescue Line. Over time, he

                                                                                        Q UANTUM S PEED
                                                                                                                                                                                                                                                                        specialized in C++ programming for Arduino, and over the
                                                                                                                                                                                                                                                                        past year, in Python for the Raspberry Pi. Currently, his
                                                                                                                                                                                                                                                                        primary responsibility is programming the line-following
                                                                                                                                                                                                                                                                        subsystem.
The entire program is built on Python, a versatile and ﬂexible
programming language known for its ease of use. Python was                                                                                                                                                                                                              Matteo Defant
selected due to its widespread adoption, particularly in image                                                                                                                                                                                                          Captain, evacuation zone programmer and 3d modelling
processing applications. The rich ecosystem of libraries that                                                                                                                                                                                                           Defant, the team captain, was primarily responsible for
Python offers was a decisive factor in our choice to use it for                                                                                                                                                                                                         programming the evacuation zone. Since 2025, he has
our robot. Libraries such as OpenCV and NumPy contribute
signiﬁcantly to our code's functionality, efﬁciently handling                                “DA VINCI” HIGH SCHOOL (TN) - ITALY                                                            RESCUE LINE                                                                 developed and implemented a machine learning system
                                                                                                                                                                                                                                                                        to recognize and classify the victims. In addition, he
heavy computational workloads.                                                                                                                                                                                                                                          designs the robot in 3D using Fusion 360 and has taken
                                                                                                                                                                                                                                                                        care of its assembly.

                                                                                                                                       HARDWARE                                                                                                                                          Chassis and wheels
    Rescue mechanism                                       4x SERVOMOTORS                                    6x MICROSWITCHES
                                                                                                        Two are used to detect
                                                                                                                                               2x DC-DC CONVERTERS
                                                                                                                                                     Step Down
                                                                                                                                                                                             ADAFRUIT
                                                                                                                                                                                         LSM6DSOX+LIS3MDL
                                                                  9g                                    whether the robot has               One step-down converter is              A sensor used to measure                                           MULTIPLEXER
                         The    victims      collection   They close the gripper                        collected the victim, while         used to step down the battery           3-axis inclination. It is utilized                       An I2C multiplexer, required
                                                          arms of the collection                        the others are used to              voltage from 7.4V to 5V for the         for detecting slopes and                                 to expand I2C addresses and
                         system       was      entirely   mechanism and open                            prevent the robot from              Raspberry Pi, while the other           ramps, as well as for executing                          communicate with multiple
                         designed and 3D-printed          the exit channels.                            getting stuck.                      powers the servomotors.                 more accurate turns.                                     sensors simultaneously.
                                                          They      offer     an
                         by us. The mechanism             excellent power to
                         consists of a ripper with        weight ratio.
                         two independent arms, so
                                                            RASPBERRY PI 5                                                                                                                                                                   2x OVONIC LiPo BATTERIES
                         that when a victim is                   8GB                                                                                                                                                                         We use two batteries: the
                         collected, it is then guided     Single-board                                                                                                                                                                       ﬁrst, rated at 5200mAh and
                         into the correct channel.        computer; after several                                                                                                                                                            7.4V, powers the Raspberry
                                                          tests, we opted for this                                                                                                                                                           Pi, while the second, rated at      Since our ﬁrst partecipation, we decided to
                         Inside each arm, there is        solution due to its                                                                                                                                                                2200mAh and 11.1V, powers
a microswitch to detect whether the victim has            versatility         and                                                                                                                                                            the motors and sensors.             custom-build the entire robot ourselves, with the
                                                          computing power.                                                                                                                                                                                                       exception of the sensors. The chassis was 3D
been caught. Additionally, there is a circuit that,
                                                              2x PICAMERA                                                                                                                                                                                                        printed in PLA using a Bambu Lab P1P, while The
when closed, identiﬁes whether it is a silver,
                                                             MODULE 3 WIDE                                                                                                                                                                          DC-DC CONVERTER              wheels have a TPU inner skeleton with a silicone
black, or fake (gray) victim.                                                                                                                                                                                                                            Step Up
                                                          Two         wide-angle                                                                                                                                                                                                 coating . Additionally, all wiring and circuit boards
The bottom of both                                        cameras to achieve the                                                                                                                                                             One step-up converter is
                                                          maximum ﬁeld of view                                                                                                                                                               used to step up the battery         were custom-made. This approach allows us to
channels is closed by a
                                                          of both the line and                                                                                                                                                               voltage from 11.1V to 12V for       position all components exactly where we prefer.
rod connected to two                                      the evacuation zone.                                                                                                                                                               the motors.                         We placed two cameras at the front of the robot,
other         servomotors,
                                                                                                                                                                                                                                                                                 one dedicated to line following and the other for
which open only when
                                                              LOGIC LEVEL                                                                                                                                                                                                        victims recognition, ensuring that everything ﬁts
the robot reaches the                                           SHIFTER                                                                                                                                                                            RASPBERRY PI AI HAT+          together perfectly.
evacuation zone. In total,                                Used       to   enable                                                                                                                                                             A graphics accelerator used
the mechanism consists                                    communication                                                                                                                                                                      in the victims recognition
                                                          between the Raspberry                                                                                                                                                              phase to increase speed and
of ﬁve servomotors and                                    Pi (3.3V) and the LEDs                                                                                                                                                             reliability.
is printed using Bambu                                    (5V).
Lab's PLA Basic. The two arms are also highly                 2x LED STRIP
practical for separating victims if they happen to        We use two RGBW                                                                                                                                                                      4x GEARMOTOR POLOLU
be close to each other, preventing the risk of            LED strips to ensure                              SERVOMOTOR 25kG                                                               5x TOF SENSORS                                     We use four Pololu motors            THANKS TO:
                                                          consistent      lighting,                        It raises and lowers the                 SERVO DRIVER                        They are used to                                     with a 156:1 gear ratio,
collecting two at once instead of one.                    making the system                                collection mechanism,                Required to control the                 bypass       obstacles                               powered at 12V to ensure
                                                          independent            of                        providing good accuracy              servomotors    directly                 along the path and to                                both speed and power.
                                                          external illumination.                           and reliability.                     from the Raspberry Pi.                  navigate within the
                                                                                                                                                                                        evacuation zone.
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