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Physics Based Path Planning for Autonomous Tracked Vehicle in Challenging Terrain

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Abstract

This paper describes a novel physics-based path planning architecture for autonomous navigation of tracked vehicles in rough terrain conditions. Unlike conventional path planning applications for smooth and structured environments, factors such as slip, slope of the terrain, robot actuator limitations, and dynamics of robot terrain interactions must be considered for rough terrain applications. The proposed path planning method consists of a hybrid planner/simulator, which takes into account all of the above factors by simulating the closed loop motion of the robot with a low-level controller on a realistic terrain model inside a physics engine. Once a feasible path to the goal is obtained, the same low-level closed loop controller is then used to execute the proposed path on the actual robot. The proposed architecture uses the D* Lite algorithm working on a 2D grid representation of the terrain as the high-level planner, Bullet as the physics engine and a hybrid automaton as the low-level closed loop controller. The proposed method is validated both in simulation and through experiments. Inferences based on the results from simulations and experiments show that the proposed planner is more effective in providing an optimal feasible path as compared to existing methodologies, demonstrating clear advantages for rough, unstructured terrain planning. Based on the results, possible improvements to the method are proposed for future work.

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References

  1. Odedra, S., Prior, S., Karamanoglu, M.: Investigating the mobility of unmanned ground vehicles. In: Proceedings of the international conference on manufacturing and engineering systems. Huwei, Yunlin (2009)

  2. Murphy, R.R.: A decade of rescue robots. In: 2012 IEEE/RSJ international conference on intelligent robots and systems, pp. 5448–5449 (2012)

  3. Murphy, R.R., et al.: Search and rescue robotics. In: Siciliano, B. , Khatib, O. (eds.) Springer handbook of robotics, pp. 1151–1173. Berlin, Heidelberg (2008)

  4. Nagatani, K. et al.: Emergency response to the nuclear accident at the Fukushima Daiichi Nuclear Power Plants using mobile rescue robots. J. F. Robot. 30(1), 44–63 (2013)

    Article  Google Scholar 

  5. Murphy R.R.: Disaster robotics, vol. 1. The MIT Press, Cambridge (2014)

  6. Davids, A.: Urban search and rescue robots: from tragedy to technology. IEEE Intell. Syst. 17(2), 81–83 (2002)

    Google Scholar 

  7. Snyder, R.G.: Robots assist in search and rescue efforts at WTC. IEEE Robot. Autom. Mag. 8(4), 26–28 (2001)

    Google Scholar 

  8. Murphy, R., Casper, J., Hyams, J., Micire, M., Minten, B.: Mobility and sensing demands in USAR. In: 2000 26th annual conference of the ieee industrial electronics society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation. 21st Century Technologies, vol. 1, pp. 138–142 (2000)

  9. Murphy, R.R., Kravitz, J., Stover, S.L., Shoureshi, R.: Mobile robots in mine rescue and recovery. IEEE Robot. Autom. Mag. 16(2), 91–103 (2009)

    Article  Google Scholar 

  10. U.S. Army Medical Research and Material Command, “Unmanned Systems Teaming for Semi-Autonomous Casualty Extraction,” SBIR-STTR, 2017. [Online]. Available: https://www.sbir.gov/sbirsearch/detail/1319095. [Accessed: 11-Feb-2017]

  11. Beebe, M.K., Gilbert, G.R.: Robotics and unmanned systems – ‘Game changers’ for combat medical missions. In: Proc. NATO RTO-HFM 182 Symp. Adv. Technol. New Proced. Med. F. Oper. (2010)

  12. Paden, B., Cap, M., Yong, S.Z., Yershov, D., Frazzoli, E.: A survey of motion planning and control techniques for Self-Driving urban vehicles. IEEE Trans. Intell. Veh. 1(1), 33–55 (2016)

    Article  Google Scholar 

  13. LaValle, S.M.: Planning algorithms. Cambridge University Press, Cambridge (2006)

    Book  MATH  Google Scholar 

  14. Lamon, P.: 3D-Position tracking and control for all-terrain robots, 1st edn., vol. 43. Springer, Berlin (2008)

  15. Tarokh, M., McDermott, G. J.: Kinematics modeling and analyses of articulated rovers. IEEE Trans. Robot. 21(4), 539–553 (2005)

    Article  Google Scholar 

  16. Kumar, P., Saab, W., Ben-Tzvi, P.: A hybrid tracked-wheeled multi-directional mobile robot. IEEE/ASME Trans. Mechatronics, p Under revision (2017)

  17. Ben-Tzvi, P., Goldenberg, A.A., Zu, J.W.: Articulated hybrid mobile robot mechanism with compounded mobility and manipulation and on-board wireless sensor/actuator control interfaces. Mechatronics 20(6), 627–639 (2010)

    Article  Google Scholar 

  18. Currier, P.N., Wicks, A.L.: A novel method for prediction of mobile robot maneuvering spaces. J. Terramechanics 50(2), 85–97 (2013)

    Article  Google Scholar 

  19. Thrun, S. et al.: Stanley: the robot that won the DARPA grand challenge. J. F. Robot. 23(9), 661–692 (2006)

    Article  Google Scholar 

  20. Bacha, A. et al.: Odin: Team VictorTango’s entry in the DARPA urban challenge. J. F. Robot. 25(8), 467–492 (2008)

    Article  Google Scholar 

  21. Urmson, C.P. et al.: High speed navigation of unrehearsed terrain?: Red Team Technology for Grand Challenge 2004 (2004)

  22. Currier, P.N.: A method for modeling and prediction of ground vehicle dynamics and stability in autonomous systems. Virginia Polytechnic Institute and State University (2011)

  23. Chu, K., Lee, M., Sunwoo, M.: Local path planning for Off-Road autonomous driving with avoidance of static obstacles. IEEE Trans. Intell. Transp. Syst. 13(4), 1599–1616 (2012)

    Article  Google Scholar 

  24. Goswami, A.: Hierarchical Off-Road Path planning and its validation using a scaled autonomous car. Clemson university (2017)

  25. Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press. (2005)

  26. Siciliano, B, Khatib, O: Springer Handbook of Robotics. Springer-Verlag New York, Inc., Secaucus (2016)

    Book  MATH  Google Scholar 

  27. Marder-Eppstein, E., Berger, E., Foote, T., Gerkey, B., Konolige, K.: The office marathon: robust navigation in an indoor office environment. In: 2010 IEEE international conference on robotics and automation, pp. 300–307 (2010)

  28. Reina, G., Bellone, M., Spedicato, L., Giannoccaro, N.I.: 3D Traversability awareness for rough terrain mobile robots. Sens. Rev. 34(2), 220–232 (2014)

    Article  Google Scholar 

  29. Castejón, C., Boada, B.L., Blanco, D., Moreno, L.: Traversable region modeling for outdoor navigation. J. Intell. Robot. Syst. Theory Appl. 43(2–4), 175–216 (2005)

    Article  Google Scholar 

  30. Garrido, S., Moreno, L., Martín, F., Álvarez, D.: Fast marching subjected to a vector field–path planning method for mars rovers. Expert Syst. Appl. 78, 334–346 (2017)

    Article  Google Scholar 

  31. Raja, R., Dutta, A., Venkatesh, K.S.: New potential field method for rough terrain path planning using genetic algorithm for a 6-wheel rover. Rob. Auton. Syst. 72, 295–306 (2015)

    Article  Google Scholar 

  32. Amorim, D., Ventura, R.: Towards efficient path planning of a mobile robot on rough terrain, pp. 22–27 (2014)

  33. Konolige, K.: A gradient method for realtime robot control. In: Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113), vol. 1, pp. 639–646 (2000)

  34. Fan, X., et al.: Integrated planning and control of large tracked vehicles in open terrain. In: 2010 IEEE international conference on robotics and automation, pp. 4424–4430 (2010)

  35. Kelly, A., Stentz, A.: Rough terrain autonomous mobility part 2?: an active vision , predictive control approach. Auton. Robots 5, 163–198 (1998)

    Article  Google Scholar 

  36. Seraji, H., Howard, A.: Behavior-based robot navigation on challenging terrain: a fuzzy logic approach. IEEE Trans. Robot. Autom. 18(3), 308–321 (2002)

    Article  Google Scholar 

  37. Howard, A., Werger, B., Seraji, H.: Integrating terrain maps into a reactive navigation strategy. In: 2003 IEEE international conference on robotics and automation (Cat. No.03CH37422), vol. 2, pp. 2012–2017 (2003)

  38. Cherif, M., Laugier, C.: Using physical models to plan safe and executable motions for a rover moving on a terrain. In: Int. Workshop on Intelligent Robotic Systems, pp. 57–66 (1993)

  39. Iagnemma, K., Dubowsky, S.: Mobile robots in rough terrain. In: Springer tracts in advanced robotics, vol. 12, p. XII, 111, no. 8, Springer, Berlin (2004)

  40. Brunner, M., Bruggemann, B., Schulz, D.: Hierarchical rough terrain motion planning using an optimal sampling-based method. In: Proc. - IEEE Int. Conf. Robot. Autom., pp. 5539–5544 (2013)

  41. Kuwata, Y., Fiore, G.A., Teo, J., Frazzoli, E., How, J.P.: Motion planning for urban driving using RRT. In: 2008 IEEE/RSJ international conference on intelligent robots and systems, pp. 1681–1686 (2008)

  42. Pepy, R., lambert, A., Mounier, H.: Path planning using a dynamic vehicle model. 2006 2nd International Conference on Information &, Communication Tsechnologies 1(1), 781–786 (2006)

    Article  Google Scholar 

  43. LaValle, S.M., jr.J.J.K: Randomized kinodynamic planning. I. J. Robot. Res. 20(5), 378–400 (2001)

    Article  Google Scholar 

  44. Kuwata, Y., Teo, J., Fiore, G., Karaman, S., Frazzoli, E., How, J.P.: Real-Time Motion planning with applications to autonomous urban driving. IEEE Trans. Control Syst. Technol. 17(5), 1105–1118 (2009)

    Article  Google Scholar 

  45. Webb, D.J., van den Berg, J.: Kinodynamic RRT*: optimal motion planning for systems with linear differential constraints. CoRR, vol. abs/1205.5 (2012)

  46. Koenig, S., Likhachev, M.. In: Proc. Eighteenth Natl. Conf. Artif. Intell., pp. 476–483 (2002)

  47. (Tony) Stentz, A.: The Focussed d* algorithm for Real-Time replanning. In: Proceedings of the international joint conference on artificial intelligence (1995)

  48. Haug, E.J.: Computer aided kinematics and dynamics of mechanical systems. Allyn & Bacon Inc., Needham (1989)

    Google Scholar 

  49. Ladd, A.M.: Motion planning for physical simulation. Rice University (2006)

  50. Coutinho, M.G.: Guide to dynamic simulations of rigid bodies and particle systems london. Springer, London (2013)

    Book  MATH  Google Scholar 

  51. Roennau, A., Sutter, F., Heppner, G., Oberlaender, J., Dillmann, R.: Evaluation of physics engines for robotic simulations with a special focus on the dynamics of walking robots. In: 2013 16th International Conference on Advanced Robotics (ICAR), pp. 1–7 (2013)

  52. Bullet Physics Engine Ver. 2.87. [Online]. Available: http://bulletphysics.org/wordpress/. [Accessed: 11-Feb-2017]

  53. Egerstedt, M., Johansson, K., Lygeros, J., Sastry, S.: Behavior based robotics using regularized hybrid automata. In: Proceedings of the 38th IEEE conference on decision and control (Cat. No.99CH36304), vol. 4, pp. 3400–3405 (1999)

  54. Egerstedt, M.: Controls for the masses [Focus on education]. IEEE Control Syst. 33(4), 40–44 (2013)

    Article  Google Scholar 

  55. Cheng, P.C.P., LaValle, S.M.: Reducing metric sensitivity in randomized trajectory design. Proc. 2001 IEEE/RSJ Int. Conf. Intell. Robot. Syst. Expand. Soc. Role Robot. Next Millenn. (Cat. No.01CH37180) 1, 43–48 (2001)

    Google Scholar 

  56. Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Rob. Res. 30 (7), 846–894 (2011)

    Article  MATH  Google Scholar 

  57. Coppelia Robotics, V-REP. [Online]. Available: http://www.coppeliarobotics.com/. [Accessed: 11-Feb-2017]

  58. Hazevoet, J., Anders, M., Huish, I.: ANT Landscape Extension for Blender 2.6. [Online]. Available: https://wiki.blender.org/index.php/Extensions:2.6/Py/Scripts/AddMesh/ANTLandscape. [Accessed: 01-Jan-2017]

  59. Blender v2.6. [Online]. Available: https://www.blender.org/. [Accessed: 01-Jan-2017]

  60. Kumar, A., Ben-Tzvi, P.: Spatial object tracking system based on linear optical sensor arrays. IEEE Sens. J 16(22), 7933–7940 (2016)

    Article  Google Scholar 

  61. Hsu, D., Kindel, R., Latombe, J.-C., Rock, S.: Randomized kinodynamic motion planning with moving obstacles. Int. J. Rob. Res. 21(3), 233–255 (2002)

    Article  MATH  Google Scholar 

  62. Tang, S.H., Kamil, F., Khaksar, W., Zulkifli, N., Ahmad, S.: Robotic motion planning in unknown dynamic environments: existing approaches and challenges. In: 2015 IEEE Int. Symp. Robot. Int.ll. Sensors, pp. 288–294 (2015)

  63. POZYX positioning system. [Online]. Available: https://www.pozyx.io/. [Accessed: 01-Jan-2017]

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Acknowledgments

This work is supported in part by the US Army Medical Research & Material Command’s Telemedicine & Advanced Technology Research Center (TATRC), under Contract No. W81XWH-16-C-0062. The views, opinions, and/or findings contained in this report are those of the authors and should not be construed as an official Department of the Army position, policy, or decision unless so designated by other documentation.

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  1. Robotics and Mechatronics Lab, Mechanical Engineering Department, Virginia Tech, Blacksburg, VA, 24061, USA

    Bijo Sebastian & Pinhas Ben-Tzvi

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  1. Bijo Sebastian
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  2. Pinhas Ben-Tzvi
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Correspondence to Pinhas Ben-Tzvi.

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Sebastian, B., Ben-Tzvi, P. Physics Based Path Planning for Autonomous Tracked Vehicle in Challenging Terrain. J Intell Robot Syst 95, 511–526 (2019). https://doi.org/10.1007/s10846-018-0851-3

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