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Off-road Path Planning Based on Improved Ant Colony Algorithm

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Abstract

Optimal vehicle off-road path planning problem must consider surface physical properties of terrain and soil. In this paper, we firstly analyse the comprehensive influence of terrain slope and soil strength to vehicle’s off-road trafficability. Given off-road area, the GO or NO-GO tabu table of terrain gird is determined by slope angle and soil remolding cone index (RCI). By applying tabu table and grid weight table, the influence of terrain slope and soil RCI are coordinated to reduce the search scope of algorithm and improve search efficiency. Simulation results based on tracked vehicle M1A1 in off-road environment show that, improved ant colony path planning algorithm not only considers the influence of actual terrain and soil, but also improves computation efficiency. The time cost of optimal routing computation is much lower which is essential for real time off-road path planning scenarios.

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References

  1. He, X., Gao, X. Z., Yan, X., et al. (2016). Continuous mobility of mobile robots with a special ability for overcoming driving failure on rough terrain. Robotica, 1, 1–21.

    Google Scholar 

  2. Cook, J. T., Ray, L. E. & Lever, J. H. (2016). Dynamics model for mobility optimization and control of off-road tractor convoys. In IEEE American Control Conference. (pp. 6875–6880).

  3. Francisco, J. C., William, A. L., & Chakravarthini, M. S. (2016). Trafficability assessment of deformable terrain through hybrid wheel-leg sinkage detection. Journal of Field Robotics., 34(3), 1–26.

    Google Scholar 

  4. Cibulova, K., Sobotka, J. & Cibulova, K. et al. (2017). Testing of different ways of overcoming untrafficable terrain. In International Conference on Military Technologies. (pp. 313–317).

  5. Schwarz, M. & Behnke, S. (2014). Local navigation in rough terrain using omnidirectional height. In International Symposium on Robotics. (pp. 1–6).

  6. Papadakis, P. (2013). Terrain traversability analysis methods for unmanned ground vehicles: A survey. Engineering Applications of Artificial Intelligence, 26(4), 1373–1385.

    Article  Google Scholar 

  7. Jiang, K & Li, H. (2015). Path planning of robot based on ant colony algorithm. In International Conference on Electrical, Computer Engineering and Electronics. (pp. 757–761).

  8. Rudolf, N., Orhan, Y., Victor, P., et al. (2018). Robot path planning based on ant colony optimization algorithm for environments with obstacles. Improved Performance of Materials, 72, 175–184.

    Article  Google Scholar 

  9. Cao, J. (2016). Robot global path planning based on an improved ant colony algorithm. Journal of Computer and Communications, 4(2), 11–19.

    Article  Google Scholar 

  10. Parpinelli, R. S., & Lopes, H. S. (2015). A computational ecosystem for optimization: review and perspectives for future research. Memetic Computing, 7(1), 29–41.

    Article  Google Scholar 

  11. Liu, J., Yang, J., Liu, H., et al. (2016). An improved ant colony algorithm for robot path planning. Soft Computing, 1(11), 1–11.

    Google Scholar 

  12. Fan, X., & Luo, X. (2003). Optimal Path Planning for Mobile Robots Based on Intensified Ant Colony Optimization Algorithm. Proceedings of IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, 1, 131–136.

    Google Scholar 

  13. Lazarowska, A. (2015). Ship’s trajectory planning for collision avoidance at sea based on ant colony optimisation. Journal of Navigation, 68(2), 291–307.

    Article  Google Scholar 

  14. Fischer, A., & Helmberg, C. (2013). The symmetric quadratic traveling salesman problem. Mathematical Programming, 14(2), 205–254.

    Article  MathSciNet  MATH  Google Scholar 

  15. Chen, K.-Y. (2014). Development of optimal path planning based on ant colony and wireless sensor network localization techniques for an autonomous mobile service robot. IEEE International Conference on Information Science, Electronics and Electrical Engineering, 2, 954–959.

    Google Scholar 

  16. Rybansky M. (2017). Trafficability analysis through vegetation. In International Conference on Military Technologies. (pp. 207–210).

  17. Flores, A. N., Entekhabi, D., & Bras, R. L. (2014). Application of a hillslope-scale soil moisture data assimilation system to military trafficability assessment. Journal of Terramechanics, 51(2), 53–66.

    Article  Google Scholar 

  18. Yang, F., Lin, G., & Zhang, W. (2015). Terrain classification for terrain parameter estimation based on a dynamic testing system. Sensor Review, 35(4), 329–339.

    Article  Google Scholar 

  19. Cunningham, C., Wong, U., Peterson, K. M., et al. (2015). Predicting terrain traversability from thermal diffusivity. Springer Tracts in Advanced Robotics, 105, 61–74.

    Article  Google Scholar 

  20. Buyurgan, N., & Lehlou, N. (2015). A terrain risk assessment method for military surveillance applications for mobile assets. Computers and Industrial Engineering, 88, 88–99.

    Article  Google Scholar 

  21. Birkel, P. A. (2003). Terrain trafficability in modeling and simulation. Technical Paper Series. (1):16–17. Computer Simulation 28:231–234.

  22. Ciobotaru, T. (2009). Semi-empiric algorithm for assessment of the vehicle mobility. Leonardo Electronic Journal of Practices and Technologies, 8(15), 19–30.

    Google Scholar 

  23. George, L., Mason, E. & Alex B. (2016). Predicting soil strength in terms of cone index and california bearing ratio for trafficability. ERDC/GSL 1–13.

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Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant No: 61273047), the National Natural Science Foundation of China (Grant No: 61573376).

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Authors and Affiliations

  1. Army Engineering University, Nanjing, China

    Han Wang, Hongjun Zhang, Kun Wang, Chen Zhang, Chengxiang Yin & Xingdang Kang

  2. PLA Troops, Nanjing, 94860, China

    Han Wang

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  1. Han Wang
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  2. Hongjun Zhang
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  3. Kun Wang
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  4. Chen Zhang
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  5. Chengxiang Yin
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  6. Xingdang Kang
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Correspondence to Han Wang.

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Wang, H., Zhang, H., Wang, K. et al. Off-road Path Planning Based on Improved Ant Colony Algorithm. Wireless Pers Commun 102, 1705–1721 (2018). https://doi.org/10.1007/s11277-017-5229-5

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