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Unique and accurate soil parameter identification for air-cushioned robotic vehicles

Published online by Cambridge University Press:  14 September 2015

Shuo Xu*
Affiliation:
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, P. R. China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai 200072, P. R. China
Yinan Gu
Affiliation:
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, P. R. China
Jing Sun
Affiliation:
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, P. R. China
Dawei Tu
Affiliation:
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, P. R. China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai 200072, P. R. China
*
*Corresponding author. E-mail: [email protected]

Summary

On-line identification of soil parameters is a pre-condition of operating performance optimization and control for unmanned ground vehicles (UGV). Inverse calculation from measured vehicular operating parameters is a prevalent methodology. However, it inherently suffers from a multiple-solution problem caused by the coupling of soil parameters in terramechanics equations and an accuracy problem caused by the influences of state noise and measurement noise. These problems in tractive-force-related soil parameters identification were addressed here for air-cushioned vehicles (ACV) by taking advantage of their additional degree of control freedom in vertical force. To be specific, a g-function algorithm was proposed to solve the multiple-solution problem from reproductive tractive force equations; de-noising techniques consisting of mean-effect strategies, sampling points selection and sample rearrangement were employed to solve the accuracy problem. A series of experiments were conducted to evaluate these techniques at different noise levels and in different soil conditions. They got satisfactory results in terms of data utilization ratio, identification accuracy and performance stability. The contribution of the paper lies in inventing a novel algorithm for unique and accurate identification of tractive-force-related soil parameters without making any simplification to the original terramechanics equation and with robustness to variations of noise level and soil condition.

Type
Articles
Copyright
Copyright © Cambridge University Press 2015 

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References

1. Bekker, M. G., Theory of Land Locomotion (University of Michigan Press, Ann Arbor, 1956).Google Scholar
2. Bekker, M. G., Introduction to Terrain-Vehicle Systems, (University of Michigan Press, Ann Arbor, 1969).Google Scholar
3. Wong, J. Y., Theory of Ground Vehicles, 3rd ed. (John Wiley & Sons, Inc., New York, 2001).Google Scholar
4. Xu, S., Sun, C. and Tu, D., “Step-by-step identification of soil parameters by air-cushion-typed off-road robots,” Chin. J. Mech. Eng. 49 (9), pp. 111 (2013).Google Scholar
5. Ray, L. E., “Estimation of terrain forces and parameters for rigid-wheeled vehicles,” IEEE Trans. Robot. 25 (3), pp. 717726 (2009).CrossRefGoogle Scholar
6. Wojciechowski, M., “Application of artificial neural network in soil parameter identification for deep excavation numerical model,” Comput. Assist. Mech. Eng. Sci. 18, pp. 303311 (2011).Google Scholar
7. Dumond, D. A., Ray, L. and Trautmann, E., “Evaluation of terrain parameter estimation using a stochastic terrain model,” Proc. SPIE – Int. Soc. Opt. Eng. 7332, pp. 73321F (2009).Google Scholar
8. Li, L. and Sandu, C., “On the impact of cargo weight, vehicle parameters, and terrain characteristics on the prediction of traction for off-road vehicles,” J. Terramechanics, 44 (3), pp. 221238 (2007).Google Scholar
9. Golda, D., Iagnemma, K. and Dubowsky, S., “Probabilistic Modeling and Analysis of High-Speed Rough-Terrain Mobile Robots,” Proceedings of the 2004 IEEE International Conference on Robotics and Automation (ICRA 2004), New Orleans, USA, (2004) pp. 914–919.Google Scholar
10. Tarantola, A., Inverse Problem Theory and Methods for Model Parameter Estimation (SIAM, Philadelphia, 2005).Google Scholar
11. Hutangkabodee, S., Zweiri, Y. H., Seneviratne, L. D. and Althoefer, K., “Multi-solution problem for track-terrain interaction dynamics and lumped soil parameter identification,” Springer Tracts Adv. Robot. 25, pp. 517528 (2006).Google Scholar
12. Le, A. T., Rye, D. C. and Durrant-Whyte, H. F., “Estimation of Track-soil Interactions for Autonomous Tracked Vehicles,” Proceedings of the 1997 IEEE International Conference on Robotics and Automation (ICRA 1997), Albuquerque, USA, (1997) pp. 1388–1393.Google Scholar
13. Iagnemma, K., Kang, S., Shibly, H. and Dubowsky, S., “Online terrain parameters estimation for wheeled mobile robots with application to planetary rovers,” IEEE Trans. Robot. 20 (5), pp. 921927 (2004).Google Scholar
14. Hutangkabodee, S., Zweiri, Y. H., Seneviratne, L. D. and Althoefer, K., “Soil parameter identification for wheel-terrain interaction dynamics and traversability prediction,” Int. J. Autom. Comput. (IJAC) 3 (3), pp. 244251 (2006).Google Scholar
15. Ding, L., Gao, H., Deng, Z., Libing, X., Junlong, G. and Lu, Y., “An approach of identifying mechanical parameters for lunar soil based on integrated wheel-soil interaction terramechanics model of rovers,” Acta Aeronaut. Astronaut. Sin. 32 (6), pp. 11121123 (2011).Google Scholar
16. Ding, L., Gao, H., Deng, Z., Tao, J. and Xiong, L., “Wheel-soil interaction mechanics model for lunar rover: Decoupling and application,” J. Harbin Inst. Technol. 43 (1), pp. 5661 (2011).Google Scholar
17. Ding, L., Yoshida, K., Nagatani, K., Gao, H. and Deng, Z., “Parameter Identification for Planetary Soil based on a Decoupled Analytical Wheel-Soil Interaction Terramechanics Model,” Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), St. Louis, USA, (2009) pp. 4122–4127.Google Scholar
18. Xu, S., Sun, J. and Shen, J., “Energy consumption modelling for air-cushion vehicles,” Appl. Mech. Mater. 483, pp. 208213 (2014).CrossRefGoogle Scholar
19. Xu, S., Luo, Z., Yu, F., Zhou, K. and Zhang, Y., “Slip ratio control simulation for semi-track air-cushion vehicle based on total power consumption minimization,” J. Syst. Simul. 20 (16), pp. 42444247+4251 (2008).Google Scholar