Determination of robot drop location for military path planning using GIS application

Due to the uncertainties and higher risks of fatality in combat situations, Unmanned Ground Robots (UGR) may be proven to be a safer alternative for carrying out critical military missions, such as search and rescue, and reconnaissance operations. Among many issues involved in the Military Path Planning (MPP) problem, this paper discusses factors affecting drop locations of the UGR in the battlefield. A customized GISbased model which finds suitable drop locations of the UGR is developed accordingly. The objective is to reach a known target location as quickly as possible, while minimizing its energy consumption as well as intervention from the enemies distributed in the battlefield. The model is tested on a complex digital terrain, assuming there is a presence of enemy's surveillance system. The result confirms the capability of the proposed method, by indicating that the candidate drop locations are feasible without violation of the specified constraints.

[1]  P. Burrough,et al.  Principles of geographical information systems , 1998 .

[2]  Diaz de Leon S Jl,et al.  Automatic path planning for a mobile robot among obstacles of arbitrary shape. , 1998 .

[3]  Kyu Ho Park,et al.  A fast path planning by path graph optimization , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[4]  Alan R. Wagner,et al.  Integrated Mission Specification and Task Allocation for Robot Teams-Testing and Evaluation , 2007 .

[5]  Steven Dubowsky,et al.  A Genetic Algorithm Based Navigation and Planning Methodology for Planetary Robotic Exploration , 1997 .

[6]  Arthur C. Sanderson,et al.  Planning multiple paths with evolutionary speciation , 2001, IEEE Trans. Evol. Comput..

[7]  Kenta Hashimoto World Scientific Series in Robotics and Automated Systems , 1993 .

[8]  Yuan F. Zheng,et al.  Recent Trends in Mobile Robots , 1994 .

[9]  Fang Wang,et al.  A multi-agent based evolutionary artificial neural network for general navigation in unknown environments , 1999, AGENTS '99.

[10]  Juan Humberto Sossa Azuela,et al.  Automatic path planning for a mobile robot among obstacles of arbitrary shape , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[11]  Manoj K. Jha,et al.  An evolutionary path planning algorithm for military applications , 2008, 2008 IEEE International Conference on System of Systems Engineering.

[12]  Se-Young Oh,et al.  Evolving a modular neural network-based behavioral fusion using extended VFF and environment classification for mobile robot navigation , 2002, IEEE Trans. Evol. Comput..

[13]  Manuela M. Veloso,et al.  Real-Time Randomized Path Planning for Robot Navigation , 2002, RoboCup.

[14]  Alan R. Wagner,et al.  Integrated Mission Specification and Task Allocation for Robot Teams - Part 2: Testing and Evaluation , 2006 .

[15]  Zbigniew Tarapata,et al.  Military route planning in battlefield simulation: effectiveness problems and potential solutions , 2003 .

[16]  H. Yamauchi,et al.  Spaceborne path planning for unmanned ground vehicles (UGVs) , 2005, MILCOM 2005 - 2005 IEEE Military Communications Conference.

[17]  D. K. Pratihar,et al.  Fuzzy-genetic algorithms and mobile robot navigation among static obstacles , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[18]  S.X. Yang,et al.  A neural network approach to complete coverage path planning , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[19]  Fumio Kojima,et al.  Perception-based genetic algorithm for a mobile robot with fuzzy controllers , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[20]  Ray Jarvis,et al.  Path planning for a mobile robot in a rough terrain environment , 2002, Proceedings of the Third International Workshop on Robot Motion and Control, 2002. RoMoCo '02..

[21]  John J. Leonard,et al.  Adaptive Mobile Robot Navigation and Mapping , 1999, Int. J. Robotics Res..