Intelligent Automation Research Group

The Intelligent Automation Research Group is focused on the development and deployment of smart automation systems for inspection and monitoring in a wide range of industrial applications.

Intelligent automation is an emerging stream of the fourth industrial revolution. The emergence of ubiquitous computing and cheap sensing devices have enabled the automation to escape the shopfloor and become an important element of contemporary living.

In terms of applications, the Group has a number of substantial projects with different industries for the development of intelligent automation systems. For instance, the aim of the Group’s navigation project is to build the foundations required to develop intelligent safety warning systems for mobile industrial platforms.

In terms of intelligent inspection and monitoring applications, the Group is also working with food and construction industries to deploy intelligent automation solutions.

 

Key research areas

  • Computer vision
  • Statistical information fusion
  • Robotics
  • Machine learning
  • Autonomous systems

 

Key research projects

Visual intelligence for safe vehicle operation in industrial environments

The aim of this project is to develop intelligent safety devices for vehicles in loosely constrained industrial settings (like fruit and vegetable markets, construction sites, etc). The rate of accidents in such environments is high due to heavy vehicles, increased demand of urgency and negligence on part of pedestrians and drivers. We focus on a vision-based collision avoidance technology to design human assisting automated safety systems.
Bab-Hadiasahar, Suter, Hoseinnezhad, Neugebauer (LP160100662), ARC Linkage Project, 2016-2019, $356K.

Crowd tracking and visual analytics for rapidly deployable imaging devices

The project aims to develop visual analytics and machine intelligence technology for commercial time-lapse imaging platforms. The focus is to use embedded systems with the imaging platforms to introduce programmability to the time-lapse cameras. This will further be extended to develop an intelligent rapidly deployable imaging product capable of tracking crowds and particular behaviours at events.
Hoseinnezhad, Vo, Bab-Hadiasahar, Accadia (LP 160101081), ARC Linkage Project, 2016-2019 $302K.

Submarine dynamics and control

The objective of this project is to help the Maritime Division of Science and Technology (DST) Group in investigating the manoeuvring, control and propulsion hydrodynamics of submarines. This project involves studies in computational and experimental fluid dynamics, acoustics, mathematical modelling and control theory to characterise various dynamics and requirements of submarine control.
Bab-Hadiasahar, Rao, DST Group, $2.2M, 2016-2022.

Intelligent solutions for boxed beef trim export enhancement

Australia’s boxed beef export faces many challenges which cause hindrance to increasing export. These are mainly due to manual labelling of boxes and manual integrity inspection at the abattoirs. This project aims to review both these issues and develop solutions which may help increase export of boxed beef trims.
Bab-Hadiasahar, Hoseinnezhad, AMPC, $280K, 2017-2018.

Automated visual inspection and preparation of live animals for meat processing

The project aims to design an automated system capable of visual inspection and cleaning of animals for slaughter. The system will also focus on detecting animal contamination (in terms of dirt or faecal material) in lairages. The proposed cleaning station can be extended to measure animal behaviour and other characteristics.
Bab-Hadiasahar, Hoseinnezhad, Gill, AMPC, $446K, 2014-2017.

 

Key publications

  • Ruwan Tennakoon, Alireza Sadri, Reza Hoseinnezhad, and Alireza Bab-Hadiashar, “Effective Sampling: Fast Segmentation Using Robust Geometric Model Fitting,” IEEE Transactions on Image Processing, Vol. 27, Issue 9, pp. 4182–4194, 2018.
  • Amirali K. Gostar, Reza Hoseinnezhad, Weifeng Liu and Alireza Bab-Hadiashar, Sensor-management for multitarget filters via minimization of posterior dispersion, IEEE Transactions on Aerospace and Electronic Systems, Volume 53, Issue 6, pp. 2877–2884, 2017.
  • Khalid Yousif, Alireza Bab-Hadiashar, Reza Hoseinnezhad, 3D SLAM in texture-less environments using rank order statistics, Robotica, Volume 35, Issue 4, pp. 809–831, 2017.
  • Ruwan Tennakoon, Alireza Bab-Hadiashar, Zhenwei Cao, Reza Hoseinnezhad, David Suter, Robust model fitting using higher than minimal subset sampling, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 38, Number 2, pp. 350–362, 2016.
  • Amir Dadashnialehi, Alireza Bab-Hadiashar, Zhenwei Cao, Ajay Kapoor 2014, "Intelligent Sensorless ABS for In-Wheel Electric Vehicles", IEEE Transactions on Industrial Electronics, vol.61, no.4, pp.1957-1969, 2014.
  • Hamid Khayyam, Alireza Bab-Hadiashar, “Adaptive intelligent energy management system of plug-in hybrid electric vehicle”, Energy, Volume 69: 319-335, 2014.
  • Alireza Bab-Hadiashar, Ruwan Tenekon, Marleen de Bruijne, “Quantification of Smoothing Requirement for 3D Optic Flow Calculation of Volumetric Images”, IEEE Transactions on Image Processing, 22(6): 2128-2137, 2013.

 

Members

Group leader

Ali Bab-Hadiashar

Academic staff

Research fellows

Research candidates

  • Van Duong Phan
  • Sundaram Muthu
  • Ayman Mukhaimar
  • Salah Mohammed Abdulrahman Ali
  • Ammar Mansoour Mehdi Saleh Kamoona
  • Sabita Panicker
  • Mohammed Imran Hossain
  • Ching Nok To
  • Varshan Beik
  • Sina Milani
  • Tania Holmes
  • Nida Ishtiaq
  • Wei Qin Chuah
  • Xan MacAtangay
  • Steven Korevar
  • Thomas Bentham
  • Mohammad Adhinehvand
  • Muhammad Shoaib

 

Opportunities

The Intelligent Automation Research Group are actively seeking to recruit research fellows and PhD students. 

 

Contact

Professor Ali Bab-Hadiashar
abh@rmit.edu.au 
+61 3 9925 6192

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Acknowledgement of country

RMIT University acknowledges the people of the Woi wurrung and Boon wurrung language groups of the eastern Kulin Nations on whose unceded lands we conduct the business of the University. RMIT University respectfully acknowledges their Ancestors and Elders, past and present. RMIT also acknowledges the Traditional Custodians and their Ancestors of the lands and waters across Australia where we conduct our business.

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