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  • Allen, T. L. & Scheding, S. J. Analysis of Solutions to the Time-Optimal Planning and Execution Problem, Journal of Intelligent Service Robotics, Volume 5, Issue 4, pp 245-258, November 2012 (Direct Link)
  • Allen, T.L. & Scheding, S.J. 'The time-optimal planning and execution problem' In Proceedings of the 2011 IEEE International Conference on Robotics and Automation, pp. 5608-5614 (Direct Link.)
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  • Allen, T. Time-Optimal Active Decision Making. Ph.D. Thesis, Department of Aerospace, Mechanical, and Mechatronic Engineering, The University of Sydney, April 2011. (Alternate Link)
  • Allen, T. Bounded anytime deflation. In Proceedings of Australasian Conference on Robotics & Automation, 2010.
  • Karumanchi, S., Allen, T., Bailey, T., and Scheding, S. Non-parametric learning to aid path planning over slopes. International Journal of Robotics Research, 2010, pp. 22. (Direct Link.)
  • Allen, T., Hill, A., Underwood, J., and Scheding, S. Dynamic Path Planning with Multi-Agent Data Fusion - The Parallel Hierarchical Replanner System. In Proceedings of International Conference on Robotics and Automation, 2009. (Direct Link.) 

This video shows a simulation of the Parallel Hierarchical Replanner, one of the main contributions of my Ph.D. thesis. In the video, an aerial vehicle flies ahead of the ground vehicle and shares the terrain information it perceives. The ground vehicle builds a cost map from the combined information and adapts both its local and global plans in real-time.

We also did this for real, with me sitting in a helicopter discovering exactly how sick one can become when you have to look at a computer screen instead of the horizon...
  • Karumanchi, S., Allen, T., Bailey, T., and Scheding, S. Non-parametric learning to aid path planning over slopes. In Proceedings of Robotics: Science and Systems, 2009. (Direct Link, Alternate link.) 

This video is actually of another colleague's work (Alex Green), however it shows off the autonomous all-terrain navigation capabilities better than any of the pictures from this paper.

My main contribution was writing a version of the A* algorithm that could assess different costs depending on the orientation of the chosen edge in the graph.
  • Allen, T., Underwood, J., and Scheding, S. A planning system for autonomous ground vehicles operating in unstructured dynamic environments. In Proceedings of Australasian Conference on Robotics and Automation, 2007. (Direct link. Alternate link.) 

The first half of this video shows the difference in performance between running my planning system without and then with prior terrain data obtained by a helicopter flyover. The screen captures show my operator GUI which indicates the overall paths taken in each case - the second one (with helicopter data) being far shorter and smoother. The second half of the video shows other people's work on the perception side of things.
  • Allen, T. Real-Time Path Planning for Autonomous Vehicles. Honours thesis, University of Sydney, 2006.

This video shows the Argo vehicle running the first real-time version my obstacle avoidance and path planning system. Raw data from the laser scanners was turned into an occupancy grid, resulting in the small tufts of grass being treated as solid obstacles. Net result? One 600kg autonomous all-terrain vehicle that carefully avoids treading on the grass.