Geesara Kulathunga — accepted AI-conference papers

2026

Navigating Narrow Spaces: A Comprehensive Framework for Agricultural Robots

Geesara Kulathunga, Abdurrahman Yilmaz, Zhuoling Huang, Ibrahim Hroob, Leonardo Guevara, Jaspreet Singh +2

ICRA 2026poster

Navigating within narrow spaces is a fundamental challenge in robotics, requiring precise localisation, localisation error recovery, dynamic path planning, and adaptive control for effective manoeuvring. This paper presents a modular and perception-driven navigation framework designed for constraine…

Cited by 0 Source ↗Scholar ↗

2025

A Genetic Approach to Gradient-Free Kinodynamic Planning in Uneven Terrains

Otobong Jerome, Alexandr Klimchik, Alexander Maloletov, Geesara Kulathunga

RA-L 2025

This paper proposes a genetic algorithm-based kinodynamic planning algorithm (GAKD) for car-like vehicles navigating uneven terrains modeled as triangular meshes. The algorithm's distinct feature is trajectory optimization over a receding horizon of fixed length using a genetic algorithm with heuris

Cited by 4 Source ↗Scholar ↗

2025

Navigating Narrow Spaces: A Comprehensive Framework for Agricultural Robots

Geesara Kulathunga, Abdurrahman Yilmaz, Zhuoling Huang, Ibrahim Hroob, Jaspreet Singh, Leonardo Guevara +2

RA-L 2025

Navigating within narrow spaces is a fundamental challenge in robotics, requiring precise localisation, localisation error recovery, dynamic path planning, and adaptive control for effective manoeuvring. This paper presents a modular and perception-driven navigation framework designed for constraine

Cited by 2 Source ↗Scholar ↗

2022

Optimization-Based Trajectory Tracking Approach for Multi-Rotor Aerial Vehicles in Unknown Environments

Geesara Kulathunga, Hany Hamed, Dmitry Devitt, Alexandr Klimchik

RA-L 2022

The goal of this paper is to develop a continuous optimization-based refinement of the reference trajectory to ‘push it out’ of the obstacle-occupied space in the global phase for Multi-rotor Aerial Vehicles in unknown environments. Our proposed approach comprises two planners: a global planner and

Cited by 17 Source ↗code ↗Scholar ↗