A GIS-based simulation model has been developed for military path planning of unmanned ground robots in a hazardous combat environment. Critical factors (such as slope of terrain, surface travel distance, degree of bumpiness in various land-uses, and exposure to enemies) that affect the path planning in a combat environment are carefully reviewed and modeled. Line-of-sight analysis and probabilistic decision making process are also modeled for the effective military path planning. In the model, the path planning is dealt with two sub-problems: (i) global path fi nding and (ii) local navigation. These two sub-problems are recursively processed during the simulation; a temporary least-cost path is calculated in the fi rst stage, and navigation of the unmanned ground robot along the path is then processed in the second stage. The simulation ends when the robot reaches a target location. The proposed model has been applied in different environments with various GIS data. The result shows that the model performs well for the military path planning, and can be useful for testing and evaluating tactical missions to be performed by the robot before its deployment in a real-world situation.
decision making, distance transformation, GIS-based simulation, military path planning
 Frederick, P., Kania, R., Rose, M.D., Ward, D., Benz, U., Baylot, A., Willis, J. & Yam-auchi, H., Spaceborne path planning for unmanned ground vehicles (UGVs), Proc. of Military Communications Conference (MILCOM 2005), IEEE, Atlantic City, NJ, 2005; http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=01606139 (accessed 2010).
 Geisler, T. & Manikas, T.W., Autonomous Robot Navigation System Using a Novel Value Encoded Genetic Algorithm, Proc., 45th Midwest Symposium on Circuits and Systems, 3, pp. 45–48, 2002.
 Sedighi, K.H., Ashenayi, K., Manikas, T.W., Wainwright, R.L. & Heng-Ming, T., Autonomous local path planning for a mobile robot using a genetic algorithm, Proc., Congress on Evolutionary Computation, 2, pp. 1338–1345, 2004.
 Yang, S.X. & Luo, C., A neural network approach to complete coverage path planning. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 34(1), pp. 718–724, 2004.
 Wang, H., Chin, Y.T., Tay, L.P., Wang, H. & William, Y.C., Vision guided AGV using distance transform, Proc., 32nd International Symposium on Robotics, pp. 19–21, 2001.
 Marzouqi, M. & Jarvis, R., Covert Path Planning in Unknown Environments with Known or Suspected Sentry Location, Proc., IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1772–1778, 2005. doi:http://dx.doi.org/10.1109/ IROS.2005.1545483
 Taylor, T., Geva, S., & Boles, W., Directed Exploration Using a Modifi ed Distance Trans-form, Proc., Digital Imaging Computing: Techniques and Applications, pp. 365–370, 2005.
 Kang, M.W., Jha, M.K. & Karri, G.A., Determination of Robot Drop Location for Mili-tary Path Planning using GIS Application, Proc., 4th WSEAS International Conference on Computer Engineering and Applications, pp. 194–200, 2010.
 Hwang, J.Y., Kim, J.S., Lim, S.S. & Park, K.H., A Fast Path Planning by Path Graph Optimization. IEEE Transactions on Systems, Man and Cybernetics, Part A, 33(1), pp. 121–129, 2003.
 Burrough, P. & McDonell, R., Principles of Geographical Information Systems, Oxford University Press: New York, 1998.