Abstract
Enabling high speed navigation of Unmanned Ground Vehicles (UGVs) in unknown rough terrain where limited or no information is available in advance requires the assessment of terrain in front of the UGV. Attempts have been made to predict the forces the terrain exerts on the UGV for the purpose of determining the maximum allowable velocity for a given terrain. However, current methods produce overly aggressive velocity profiles which could damage the UGV. This paper presents three novel safer methods of force prediction that produce effective velocity profiles. Two models, Instantaneous Elevation Change Model (IECM) and Sinusoidal Base Excitation Model: using Excitation Force (SBEM:EF), predict the forces exerted by the terrain on the vehicle at the ground contact point, while another method, Sinusoidal Base Excitation Model: using Transmitted Force (SBEM:TF), predicts the forces transmitted to the vehicle frame by the suspension.
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References
DuPont, E.M., Moore, C.A., Collins, E.G., Coyle, E.: Frequency response method for terrain classification in autonomous ground vehicles. Auton. Robot. 24(4), 337–347 (2008)
Sadhukhan, D.: Autonomous ground vehicle terrain classification using internal sensors. MIT Press (2004)
Ward, C.C., Iagnemma, K.: Speed-independent vibration-based terrain classification for passenger vehicles. Veh. Sys. Dyn. 47(9), 1095–1113 (2009)
Collins, E.G., Coyle, E.J.: Vibration-based terrain classification using surface profile input frequency responses. In: IEEE Int. Conf. on Robot. and Autom., pp. 3276–3283 (2008)
Mou, W., Kleiner, A.: Online Learning Terrain Classification for Adaptive Velocity Control. In: Int. Work. on Safety Security and Rescue Robotics, pp. 1–7 (2010)
Weiss, C., Tamimi, H., Zell, A.: A combination of vision- and vibration-based terrain classification. In: IEEE/RSJ Int.Conf. on Intell. Robots and Sys., pp. 2204–2209 (2008)
Stavens, D., Thrun, S.: A self-supervised terrain roughness estimator for off-road autonomous driving. In: Proc. of Conf. on Uncertainty in AI, UAI (2006)
Brooks, C.A.: Learning to Visually Predict Terrain Properties for Planetary Rovers. Massachusetts Institute of Technology (2009)
Chilian, A., Hirschmuller, H.: Stereo camera based navigation of mobile robots on rough terrain. In: IEEE/RSJ Int.Conf. on Intell. Robots and Sys., pp. 4571–4576 (2009)
Jin, G.-G., Lee, Y.-H., Lee, H.-S., So, M.-O.: Traversability analysis for navigation of unmanned robots. In: SICE Annu. Conf., pp. 1806–1811 (August 2008)
El-Kabbany, A., Ramirez-Serrano, A.: Terrain Roughness Assessment for High Speed Ugv Navigation in Unknown Heterogeneous Terrains. Int. J. of Inf. Acq. 7(2), 165 (2010)
Inman, D.J.: Engineering Vibration. In: Engineering Vibration, 3rd edn., pp. 130–139. Pearson Education Inc. (2008)
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Wilson, G.N., Ramirez-Serrano, A., Mustafa, M., Davies, K.A. (2012). Velocity Selection for High-Speed UGVs in Rough Unknown Terrains Using Force Prediction. In: Su, CY., Rakheja, S., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2012. Lecture Notes in Computer Science(), vol 7507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33515-0_39
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DOI: https://doi.org/10.1007/978-3-642-33515-0_39
Publisher Name: Springer, Berlin, Heidelberg
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