Enabling Methodologies for Automated Monitoring and Control of Robotic Grit-Blasting Process and Multi-Robot
Since 2005, the project team has been developing a fully autonomous steel bridge maintenance robotic system. In collaboration with the NSW Roads and Maritime Services (RMS), the team has developed methodologies that enable a robot to conduct the following tasks automatically: exploring an unknown environment, building 3D map of the environment, planning suitable blasting path and robot collision-free motion, and conducting grit-blasting. Two practically deployable robotic systems have been used in the Sydney Harbour Bridge maintenance sites since 2013.
This research project aims to extend the functionality of the grit-blasting robotic systems by developing methodologies that enable:
- Automated monitoring of robotic grit-blasting process
- Automated inspection of surface quality
- Control of the robotic system for blasting surface spots that are missed out in previous blasting operation, or where the quality is not good enough.
- Multiple grit-blasting robotic systems to operate collaboratively in a blasting environment.
Chief Investigators
Other Members
- Shoudong Huang
- Andrew To
- Mahdi Hassan
Funding:
- SABRE Autonomous Solutions
Years
- 2013–2014
Relevant publications
- Mahdi Hassan, Dikai Liu, Shoudong Huang, Gamini Dissanayake, “Task Oriented Area Partitioning and Allocation for Optimal Operation of Multiple Industrial Robots in Unstructured Environments”, to appear in Proceedings of the 13th International Conference on Control, Automation, Robotics and Vision (ICARCV 2014), December 10-12, 2014, Singapore
- To, A.W., Paul, G. & Liu, D., 'Surface-type Classification Using RGB-D', IEEE Transactions on Automation Science and Engineering, Vol. 11, Issue 2, pp.359-366, April 2014
- To, A. W. K., Paul, G., Smith, D. R., Liu, D. K. & Dissanayake, G. Automated and Frequent Calibration of a Robot Manipulator-mounted IR Range Camera for Steel Bridge Maintenance, Proceedings of the International Conference on Field and Service Robotics, 2012, pp1 – 14
- To, A.W., Paul, G. & Liu, D. 2010, 'Image Segmentation for Surface Material-type Classification using 3D Geometry Information', IEEE International Conference on Information and Automation, Harbin, China, June 2010 in Proceedings of the 2010 IEEE International Conference on Information and Automation (ICIA2010), ed X. Ye, et al, IEEE, China, pp. 1717-1722.