IA Cobotics Lab
Through our research in these diverse areas, we strive to unlock the true potential of cobotics, making it an integral part of our future in various industries and domains; Our research spans across several key areas, including:
- Autonomous cobots and automation
- Robotic vision and sensor fusion
- AI-powered intelligence for cobotics
- Strategies for Human-Robot collaboration
- Rapidly integrable solutions
- Soft robotics
- Healthcare collaborative robots
- Evaluate, deploy, manage, and monitor robots in various applications
Academic Research Team
Dr Ehsan Asadi
Team Leader, Robotics, IEEE Senior Member, A/Editor IEEE RA-L
Prof Alireza Bab-Hadiashar
Research Leader, Visual-AI, Intelligent Automation (IA) Group
Prof Reza Hoseinnezhad
Multi-Sensor Fusion & Machine Vision
Dr Amirali K-Gostar
Multi-Sensor Fusion & Machine Vision
Dr Ruwan Tennakoon
AI and Machine Learning
Dr Hamid Khayyam
Energy and Machine Learning
HDR Research Team
Christian Milianti
HDR Candidate
Data-efficient Machine Learning for Robotic Perception and Manipulation
Thilina Tharanga Malagalage Don
HDR Candidate
Extended Robotic Reality for Robot Learning
Shanuka Dodampegama
HDR Candidate
Robotic Intelligence for Construction Waste Sorting
Subash Gautam
HDR Candidate
Vision Systems for Processes Control in Metal Additive Manufacturing
Shayan Azizi
HDR Candidate
Developing the Next Generation Materials Science Lab
Umair Naeem
HDR Candidate
RESEARCH advisers
Prof Ivan Cole
Additive Manufacturing
Prof Olga Troynikov
Human-Centered Automation
Prof Stuart Bateman
Aerospace & Advanced Manufacturing
Prof Mark Easton
Manufacturing & Materials
Research Projects
Intelligent Automation and Service Robots for Infrastructure and Construction
Deploying robots in collaboration with humans is seen as an enabler of major changes in construction productivity for various tasks, such as digital twain, quality/compliance inspection, progress monitoring and automated interior finishing.
Robot Mechanisms and Systems Design
IEEE Transactions on Industrial Electronics, 2020 IROS 2020 Mechanism and Machine Theory Robotics and Computer-Integrated Manufacturing FToMM Symposium on Robot Design, Dynamics and Control
Extended Reality (XR) in Robotics
XR devices offer a range of spatial perception capabilities that can enhance human and robot collaboration. From 6DoF tracking to depth perception, gesture recognition, and environmental mapping, these features enable applications spanning human and robot interaction for manufacturing, healthcare, and beyond, XR devices continue to push the boundaries of spatial perception and redefine how we interact with robots
Robotic Vision and Perception
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Robotics Additive Manufacturing
Robotic additive manufacturing is a technology that can deposit material and fabricate complex parts. Part geometry during the process can negatively affect the shape and size of the final manufactured object. In-process spatio-temporal 3D reconstruction, also known as 4D reconstruction, allows for early detection of deviations from the design in robotic additive manufacturing, thus providing the opportunity to rectify at an early stage, making the process more robust, efficient and productive.
AI-Driven Solar Panel Inspection
In our study, aerial images from the suburbs of Melbourne revealed two primary types of soiling on PV panels: dust, attributable to wind and climate conditions, and bird droppings, distinct from vegetation debris like leaves or shading effects. Soiling on solar panels directly impacts their efficiency, with bird droppings, despite their small size, having a substantial effect on overall output. The size and specific location of these droppings are critical factors in determining the extent of power loss. Over time, bird droppings can lead to more severe, long-term issues, including permanent panel damage, primarily due to hotspots created by their accumulation. These soiling types require different cleaning approaches; dust can be removed with low-pressure water jets, whereas bird droppings require a specialized cleaning solution for effective removal. Consequently, our dataset was curated to reflect these cleaning requirements. It was observed that the ratio of dust to bird droppings on soiled PV panels averages 1:2, leading to a class imbalance challenge in the dataset. Further complicating detection, bird droppings are small, lack distinct spatial or color features, and generally cover less than 2% of a panel's surface, contrasting with larger dust patches. The panels, as detailed in Table , are of the Polycrystalline type with a blue background, where bird droppings typically appear white or grayish, without a specific shape. These factors collectively contribute to the complexity of accurately detecting soiling on PV panels.
Robotic Challenges and Capstones
Advanced Robotics System Course
Post Graduate Students, Engagement with Cobots 2023
Contact
- 58 Cardigan St, Carlton Melbourne, Victoria, Australia