IJAT Vol.6 No.6 pp. 749-756
doi: 10.20965/ijat.2012.p0749


A Multiple Level of Detail Approach to the Tactical Movement Problem

Peter Beasley and P. Ross McAree

School of Mechanical of Mining Engineering, University of Queensland, Brisbane 4072, Australia

April 27, 2012
September 18, 2012
November 5, 2012
task planning, mobile manipulator, excavation, optimisation
The tactical movement problemis considered to be one in which a robotic agent is required to move around its world to complete a task. This agent has manipulation abilities which allow it to perform work on its local surroundings. The coupled optimisation of the agent movements and manipulations is thus of key importance to minimise the cost of completing the task. The driving practical application in this paper is one of cost effective excavation in a mining environment. The agent is a mining shovel and it has the ability to manipulate the world through excavation actions. The problem becomes one of determining the optimal path that the shovel should take and the dig operations that should be completed at each point along the path. An initial solution is presented to automatically generate an optimized dig plan for a large robotic excavator. A wavelet based detail reduction approach is used which allows a near optimal solution of the problem to be generated in practically useful timeframes.
Cite this article as:
P. Beasley and P. McAree, “A Multiple Level of Detail Approach to the Tactical Movement Problem,” Int. J. Automation Technol., Vol.6 No.6, pp. 749-756, 2012.
Data files:
  1. [1] W. Powell, “Approximate Dynamic Programming,” Hoboken, John Wiley and Sons, 2007.
  2. [2] S. Singh and R. Simmons, “Task Planning For Robotic Excavation,” Intelligent Robots and Systems, Proc. of the 1992 lEEE/RSJ Int. Conf. on, 1992, pp. 1284-1291.
  3. [3] S. Singh, “Synthesis of tactical plans for robotic excavation,” Ph.D. Thesis, Pittsburgh, PA, Carnegie Mellon University, 1995.
  4. [4] S. Singh, “Multi-Resolution Planning for Earthmoving,” Int. Conf. on Robotics and Automation, 1998.
  5. [5] Y. Kuwata, “Real-time trajectory design for unmanned aerial vehicles using receding horizontal control,” p. 151, 2003.
  6. [6] T. Schouwenaars, “Safe trajectory planning of autonomous vehicles,” Aeronautics and Astronautics, Massachusetts Institute of Technology, 2006.
  7. [7] M. Alighanbari, “Task assignment algorithms for teams of UAVs in dynamic environments,” Aeronautics and Astronautics, Massachusetts Institute of Technology, 2004.
  8. [8] D. Jung, “Multiresolution On-Line Path Planning for Small Unmanned Aerial Vehicles,” Americal Control Conf. Seattle, Washington, USA, 2008, pp. 2744-2749.
  9. [9] K. Dinesh, “Multiresolution Rough Terrain Motion Planning,” IEEE Trans. on Robotics and Automation, Vol.14, pp. 19-32, 1998.
  10. [10] J. Wolfe, “Combined Task and Motion Planning,” Proc. of the Twentieth Int. Conf. on Automated Planning and Scheduling, 2010, pp. 254-257.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 22, 2024