Team LCASTOR
Team members
Rajitha de Silva, Jonathan Cox, Elliot Smith, Emmanuel Soumo, Yasmin Alderson, Amy‒Rose Masic
Team captain’s name
Rajitha de Silva
Instructor(s)
Dr. Riccardo Polvara, University of Lincoln, UK
Participating in the FRE since
2025
Description of the team and robot
LCASTOR is the University of Lincoln’s robotics competition team, uniting postdocs, PhD, master’s and undergraduate students from the Lincoln Centre for Autonomous Systems (LCAS) and Lincoln Institute for Agricultural Technology (LIAT). For FRE 2026, we will represent the UK with an improved holonomic AgileX Ranger Mini field robot, strengthened by technologies emerging from our students’ research outputs, to tackle crop-row navigation, weed and pest detection, and freestyle agri-tech demonstrations.
Robot specifications
W x L x H (cm):
50.0 × 73.8 × 33.8
Weight (kg):
75
Commercial or prototype:
Commercial
Total no. of wheels / no. driven wheels:
4/4
Drivetrain concept/ max. speed (m/s):
4WD omnidirectional/holonomic platform with four-wheel independent steering and in-wheel drive motors; max. speed approx. 1.5–1.67 m/s
Turning radius (cm):
0 cm in spin mode; approx. 81 cm in Ackermann mode
Battery type / capacity (Ah):
48 V lithium iron phosphate battery / 24 Ah
Total motor power (W):
1,800 W total installed motor power: 4 × 350 W drive motors + 4 × 100 W steering motors
No. of sensors internal/
external:
Sensor type:
Base platform: CAN feedback, battery/BMS status, motor/drive feedback.
External: LiDAR, RGB/depth cameras, IMU
Controller system software description:
The platform supports command control via CAN and secondary development using AgileX software/SDK resources. For FRE 2026, the LCASTOR system can combine sensor data analysis, autonomous crop-row navigation, perception for weed/pest detection, and machine-control logic developed from student research outputs.
Controller system hardware description:
AgileX Ranger Mini 2.0 chassis with integrated drive and steering motor control, CAN communication interface, 48 V battery system, external power interface, and a team-added NVIDIA Jetson AGX Orin / Orin Nano onboard computer for perception, navigation, sensor-data processing, and high-level robot control, together with the required sensing payload.
Short strategy description for navigation and applications:
The robot will use row following and row switching as separate navigation behaviours, with the overall mission orchestrated through Nav2 behaviour trees. An independent vision system will run in parallel for object detection, enabling vision-based actions such as weed, pest, crop or obstacle identification to be triggered during autonomous operation.
These are the commercial team sponsors & partners:
AgriForwards CDT at University of Lincoln
, Douglas Bomford Trust