Ryan Lim — accepted AI-conference papers

2024

A Robust High-Strength Multi-Surface Rapid UV-Curable Payload Installation System for Generic Multirotors via Impact Delivery

Ryan Lim, Jeck Tan, Matthew Ng, Hong Yee Low, Shaohui Foong

RA-L 2024

This letter details the design and development of a novel 3D-printed, lightweight and rapid-curing automated payload installation system for aerial robots, using a 3D printed resin-filled adhesive carrier tile (ACT). Its structure is designed to fracture and disperse ultraviolet (UV) curable resin o

Cited by 0 Source ↗Scholar ↗

2022

Monocular UAV Localisation With Deep Learning and Uncertainty Propagation

Xueyan Oh, Ryan Lim, Leonard Loh, Chee How Tan, Shaohui Foong, U-Xuan Tan

RA-L 2022

In this paper, we propose a ground-based monocular UAV localisation system that detects and localises an LED marker attached to the underside of a UAV. Our system removes the need for extensive infrastructure and calibration unlike existing technologies such as UWB, radio frequency and multi-camera

Cited by 9 Source ↗Scholar ↗