R. Khorrambakht — accepted AI-conference papers

2024

A Graph-Based Self-Calibration Technique for Cable-Driven Robots with Sagging Cable

M. R. Dindarloo, A. S. Mirjalili, S. A. Khalilpour, R. Khorrambakht, Stephan Weiss, H. D. Taghirad

IROS 2024poster

The efficient operation of large-scale Cable-Driven Parallel Robots (CDPRs) relies on precise calibration of kinematic parameters and the simplicity of the calibration process. This paper presents a graph-based self-calibration framework that explicitly addresses cable sag effects and facilitates th…

Cited by 0 Source ↗Scholar ↗

2023

Graph-Based Visual-Kinematic Fusion and Monte Carlo Initialization for Fast-Deployable Cable-Driven Robots

R. Khorrambakht, H. Damirchi, M.R. Dindarloo, A. Saki, S.A. Khalilpour, Hamid D. Taghirad +1

IROS 2023poster

Ease of calibration and high-accuracy task-space state-estimation purely based on onboard sensors is a key requirement for enabling easily deployable cable robots in real-world applications. In this work, we incorporate the onboard camera and kinematic sensors to drive a statistical fusion framework…

Cited by 2 Source ↗Scholar ↗