Team RoboTO
Team members
Letizia D’Angelo, Matteo Vecchia, Francesca Itta, Niccolò Cossu, Niccolò Malgeri, Dario Lupo, Vitor Viana de Paula, Marco Ambrosio, Alessandro Navone, Umberto Albertin
Team captain’s name
Letizia D’Angelo
Instructor(s)
Marco Ambrosio, Alessandro Navone, Umberto Albertin, Politecnico di Torino, Interdepartmental Centre for Service Robotics (PIC4SeR), Turin, Italy
Participating in the FRE
for the second time
Description of the team and robot
Team Roboto from Politecnico di Torino is a coalition of engineering students from various backgrounds. Split into 5 divisions, we are united by a single goal: engineering the future, one robot at a time. We build the perfect environment to develop robots with special technical features every year. We take part in international competitions and are proud to be the first European team to compete in the RoboMaster University League. Today we continue to grow, aiming at new challenges and global events like FRE 2026.
Robot specifications
W x L x H (cm):
45 x 60 x 35
Weight (kg):
15
Commercial or prototype:
Prototype
Total no. of wheels / no. driven wheels:
4/4
Drivetrain concept/ max. speed (m/s):
4,75 m/s
Turning radius (cm):
-
Battery type / capacity (Ah):
6,67 Ah
Total motor power (W):
1000
No. of sensors internal/
external:
Sensor type:
3 diff erent type of sensors:
- 3D LIDAR
- Depth camera
- IMU
Controller system software description:
- 3D LIDAR - Livox MID-360 - Point cloud fi ltering and segmentation
- 3x Depth camera - Realsense D455 - Detection of object through DNNs image processing
- IMU - Dead reckoning and state estimation
Controller system hardware description:
- Compute Unit- Nvidia Jetson Orin NX 64GB
- Traction Motor Controllers - 4x DJI RoboMaster C620 - Speed control via CAN-bus for the traction motors (M3508).
- Steering Motor Controllers - 4x Integrated MIT mode Drivers (CubeMars AK70-10) - Position control via CAN-bus for independent wheel orientation
- Low-Level Microcontroller - DJI RoboMaster Development Board Type C with STM32 main controller- Real-time calculation of electronic Ackermann/swerve steering kinematics and centralized CAN-bus communication with all motor drivers.
Short strategy description for navigation and applications:
- Task 1, 2, 3 - Maize fi eld navigation: this task is performed by analyzing the point cloud provided by the 3D LIDAR. It allows our robot to infer where the maize plants are located and to fi nd the best path in between the rows. When the end of the row is detected an heuristic algorithm is used to infer where the other rows are and to enter in the next correct row.
- Task 4 - Open fi eld navigation: to fulfi l this task our team exploits state-of-the-art SLAM algorithms and a smart heuristic to cover all the working fi eld and to approach the point of interest with a convenient attitude.
These are the commercial team sponsors & partners:
- Politecnico di Torino
- PIC4SeR