Mr Andrew Loeliger

Data Science Support Student

A smart sensor box to increase the adaptability of automated manufacturing: Loeliger Andrew, Yang Erfu, Bomphray Iain; Advances in Transdisciplinary Engineering Vol 25, pp. 31-36 (2022); https://doi.org/10.3233/atde220561
High-precision UWB based localisation for UAV in extremely confined environments: Yang Beiya, Yang Erfu, Yu Leijian, Loeliger Andrew; IEEE Sensors Journal Vol 22, pp. 1020-1029 (2022); https://doi.org/10.1109/JSEN.2021.3130724
An overview of automated manufacturing for composite materials: Loeliger Andrew, Yang Erfu, Bomphray Iain; 2021 26th International Conference on Automation and Computing (ICAC) The 26th International Conference on Automation & Computing, pp. 1-6 (2021); https://doi.org/10.23919/ICAC50006.2021.9594159
Stereo vision-based autonomous navigation for oil and gas pressure vessel inspection using a low-cost UAV: Yu Leijian, Yang Erfu, Yang Beiya, Loeliger Andrew, Fei Zixiang; 2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021 , pp. 1052-1057 (2021); https://doi.org/10.1109/RCAR52367.2021.9517584

Autonomous Manufacturing of Composite Products with Multiple Flexible and Intelligent Robots: Yang, Erfu (Principal Investigator) Bomphray, Iain (Co-investigator) Loeliger, Andrew (Researcher); The ambition of this project is to fundamentally investigate the novel adaptive control algorithms and smart path planning strategies for developing a feasible solution to the autonomous manufacturing of composite products through the use of a MAR system. A highly-efficient vision system is also to be investigated by utilising advanced machine learning algorithms to detect the tools and materials, which is more intelligent and can significantly reduce human’s work. The proposed adaptive control algorithms and smart path planning strategies consist of an intelligent MAR controller and in-process path planner that determines an optimal path in a collaborative manner of multiple robots in the flexible manufacturing environment has raised the significant challenges in both academic and industrial domains It is also proposed that the MAR will have obstacle avoidance capabilities to avoid collision with other machines and humans in the shared workfloor; 01-Jan-2020 - 30-Jan-2023