Skip to main content

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSCONTROL))

  • 1514 Accesses

Abstract

The popularity of the research on unmanned ground vehicles has increased recently due to a variety of operations and environments. Planetary explorations, search and rescue missions in hazard areas, surveillance, humanitarian de-mining, as well as agriculture applications such as pruning vine and fruit trees, represent possible fields of using autonomous vehicles in natural environments. Planetary exploration allows for understanding the planet surface geology, its present and past climate conditions, and for discovering potential signs of other lives.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. J. Latombe, Robot Motion Planning (Kluwer, Boston, 1991)

    Google Scholar 

  2. S.M. LaValle, Planning Algorithms (Cambridge University Press, Cambridge, 2006)

    Google Scholar 

  3. O. Khatib, Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Rob. Res. 5(1), 90–98 (1986)

    Article  MathSciNet  Google Scholar 

  4. H. Haddad, M. Khatib, S. Lacroix, R. Chatila, Reactive navigation in outdoor environments using potential fields. in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1232–1237 (1998)

    Google Scholar 

  5. Y. Koren, J. Borenstein, Potential field methods and their inherent limitations for mobile robot navigation. in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1398–1404 (1991)

    Google Scholar 

  6. B. Chanclou, A. Luciani, Global and local path planning in natural environment by physical modeling. in Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1118–1125 (1996)

    Google Scholar 

  7. S. Sekhavat, M. Chyba, Nonholonomic deformation of a potential field for motion planning. in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 817–822 (1999)

    Google Scholar 

  8. K.P. Valavanis, T. Hebert, R. Kolluru, N. Tsourveloudis, Mobile robot navigation in 2-d dynamic environments using an electrostatic potential field. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 30(2), 187–196 (2000)

    Google Scholar 

  9. S.S. Ge, Y.J. Cui, Dynamic motion planning for mobile robots using potential field method. Auton. Robots 13(3), 207–222 (2002)

    Article  MATH  Google Scholar 

  10. S. Caselli, M. Reggiani, R. Sbravati, Parallel path planning with multiple evasion strategies. in Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1, pp. 260–266 (2002)

    Google Scholar 

  11. C.I. Connolly, J.B. Burns, R. Weiss, Path planning using Laplace’s equation. in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2102–2106 (1990)

    Google Scholar 

  12. E. Rimon, Exact robot navigation using artificial potential functions, Ph.D. thesis, 1990

    Google Scholar 

  13. E. Rimon, D.E. Koditschek, Exact robot navigation using artificial potential fields. IEEE Trans. Rob Autom. 8, 501–518 (1992)

    Google Scholar 

  14. O. Brock, O. Khatib, High-speed navigation using the global dynamic window approach. in Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1, pp. 341–346 (1999)

    Google Scholar 

  15. H. Tanner, S. Loizou, K. Kyriakopoulos, Nonholonomic navigation and control of cooperating mobile manipulators. IEEE Trans. Robot. Autom. 19(1), 53–64 (2003)

    Google Scholar 

  16. S. Shimoda, Y. Kuroda, K. Iagnemma, High-speed navigation of unmanned ground vehicles on uneven terrain using potential fields. Robotica 25(4), 409–424 (2007)

    Google Scholar 

  17. Z. Shiller, J. Chen, Optimal motion planning of autonomous vehicles in three dimensional terrains. in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 198–203 (1990)

    Google Scholar 

  18. Z. Shiller, Y.-R. Gwo, Dynamic motion planning of autonomous vehicles. IEEE Trans. Robot. Autom. 7(2), 241–249 (1991)

    Article  Google Scholar 

  19. P. Fiorini, Z. Shiller, Motion planning in dynamic environments using velocity obstacles. Int. J. Robot. Res. 17, 760–772 (1998)

    Article  Google Scholar 

  20. J. Borenstein, Y. Koren, The vector field histogram—fast obstacle avoidance for mobile robots. IEEE J. Robot. Autom. 7(3), 278–288 (1991)

    Article  Google Scholar 

  21. J. Minguez, L. Montano, Nearness diagram (nd) navigation: collision avoidance in troublesome scenarios. IEEE Trans. Robot. Autom. 20(1), 45–59 (2004)

    Article  Google Scholar 

  22. R. Simmons, The curvature-velocity method for local obstacle avoidance, in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 3375–3382 (1996)

    Google Scholar 

  23. D. Fox, W. Burgard, S. Thrun, The dynamic window approach to collision avoidance. IEEE Robot. Autom. Mag. 4, 23–33 (1997)

    Google Scholar 

  24. R. Philippsen, R. Siegwart, Smooth and efficient obstacle avoidance for a tour guide robot. in Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1, pp. 446–451 (2003)

    Google Scholar 

  25. M. Spenko, Y. Kuroda, S. Dubowsky, K. Iagnemma, Hazard avoidance for high speed unmanned ground vehicles in rough terrain. J. Field Robot. 23(5), 311–331 (2006)

    Article  MATH  Google Scholar 

  26. M. Spenko, Hazard avoidance for high speed rough terrain unmanned ground vehicles, Ph.D. thesis, Massachusetts Institute of Technology, MA, 2005

    Google Scholar 

  27. P. Oegren, N.E. Leonard, A convergent dynamic window approach to obstacle avoidance. IEEE Trans. Robot. 21(2), 188–195 (2005)

    Article  Google Scholar 

  28. P. Oegren, N.E. Leonard, A provably convergent dynamic window approach to obstacle avoidance. in Proceedings of IFAC World Congress, pp. 595–600 (2001)

    Google Scholar 

  29. L.E. Dubins, On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents. Am. J. Math. 79(3), 497–516 (1957)

    Article  MathSciNet  MATH  Google Scholar 

  30. E. Frazzoli, M.A. Dahleh, E. Feron, Real-time motion planning for agile autonomous vehicles. J. Guidance Control Dyn. 1(25), 116–129 (2002)

    Google Scholar 

  31. T.M. Howard, A. Kelly, Optimal rough terrain trajectory generation for wheeled mobile robots. Int. J. Rob. Res. 26(2), 141–166 (2007)

    Article  Google Scholar 

  32. C.J. Green, A. Kelly, Toward optimal sampling in the space of paths. in Proceedings of the International Symposium of Robotics Research, pp. 171–180 (2007)

    Google Scholar 

  33. T.M. Howard, C.J. Green, A. Kelly, D. Ferguson, State space sampling of feasible motions for high-performance mobile robot navigation in complex environments. J. Field Robot. 25(10), 325–345 (2008)

    Article  Google Scholar 

  34. M. Pivtoraiko, R.A. Knepper, A. Kelly, Differentially constrained mobile robot motion planning in state lattices. J. Field Robot. 26(3), 308–333 (2009)

    Article  Google Scholar 

  35. P. Hart, N. Nilsson, B. Raphael, A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)

    Article  Google Scholar 

  36. M. Pivtoraiko, A. Kelly, Efficient constrained path planning via search in state lattices. in Proceedings of the 8th International Symposium on Artificial Intelligence, Robotics and Automation in Space, vol. 8, Sept 2005

    Google Scholar 

  37. R. A. Knepper, A. Kelly, High performance state lattice planning using heuristic look-up tables. in Proceedings of 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3375–3380, Oct 2006

    Google Scholar 

  38. A. Stentz, The focussed D* algorithm for real-time replanning. in Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1652–1659 (1995)

    Google Scholar 

  39. S. Koenig, M. Likhachev, Fast replanning for navigation in unknown terrain. IEEE Trans. Robot. 21(3), 354–363 (2005)

    Article  Google Scholar 

  40. J. Barraquand, J. Latombe, Motion planning: a distributed representation approach. Int. J. Robot. Res. 10(6), 628–649 (1991)

    Google Scholar 

  41. L.E. Kavraki, Random networks in configuration space for fast path planning, Ph.D. thesis, 1995

    Google Scholar 

  42. M.H. Overmars, P. Å vestka, A probabilistic learning approach to motion planning. in WAFR: Proceedings of the Workshop on Algorithmic Foundations of Robotics (1995)

    Google Scholar 

  43. L. Kavraki, P. Svestka, J. Latombe, M. Overmars, Probabilistic roadmaps for fast path planning in high dimensional configuration spaces. IEEE Trans. Robot. Autom. 12, 566–580 (1996)

    Google Scholar 

  44. H. Choset, K.M. Lynch, S. Hutchinson, G. Kantor, W. Burgard, L. Kavraki, S. Thrun, Principles of Robot Motion: Theory, Algorithms, and Implementations (MIT Press, Cambridge, 2005)

    Google Scholar 

  45. D. Hsu, J.-C. Latombe, H. Kurniawati, On the probabilistic foundations of probabilistic roadmap planning. Int. J. Rob. Res. 25(7), 627–643 (2006)

    Google Scholar 

  46. G. Song, N.M. Amato, Randomized motion planning for car-like robots with C-PRM. in Proceedings of the 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 37–42 (2001)

    Google Scholar 

  47. M.S. Branicky, S.M. Lavalle, K. Olson, L. Yang, Quasi-randomized path planning. in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1481–1487 (2001)

    Google Scholar 

  48. J. Kuffner, S.M. Lavalle, Randomized kinodynamic planning. in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 473–479 (1999)

    Google Scholar 

  49. S.M. Lavalle, J. Kuffner, Rapidly-exploring random trees: progress and prospects. in Algorithmic and Computational Robotics: New Directions (AK Petetrs Ltd., Welleslry, 2001), pp. 293–308

    Google Scholar 

  50. S.M. Lavalle, J. Kuffner, RRT-connect: an efficient approach to single-query path planning. in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 995–1001 (2000)

    Google Scholar 

  51. D. Hsu, R. Kindel, J.-C. Latombe, S. Rock, Randomized kinodynamic motion planning with moving obstacles. in Algorithmic and Computational Robotics: New Directions, pp. 247–264 (2001)

    Google Scholar 

  52. D.J. Spero, R.A. Jarvis, Path planning for a mobile robot in a rough terrain environment. in Third International Workshop on Robot Motion and Control, pp. 9–11 (2002)

    Google Scholar 

  53. M. Kobilarov, G.S. Sukhatme, Time optimal path planning on outdoor terrain for mobile robots under dynamic constraints. Unpublished research paper from the USC Center for Robotics and Embedded Systems Lab., 2004

    Google Scholar 

  54. A. Ettlin, H. Bleuler, Randomised rough-terrain robot motion planning. in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5798–5803 (2006)

    Google Scholar 

  55. D. Ferguson, A. Stentz, Anytime RRTs. in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5369–5375 (2006)

    Google Scholar 

  56. S. Karaman, M.R. Walter, A. Perez, E. Frazzoli, S. Teller, Anytime motion planning using the RRT. in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1478–1483 (2011)

    Google Scholar 

  57. J.A. Primbs, V. Nevistic, J.C. Doyle, Nonlinear optimal control: a control Lyapunov function and receding horizon perspective. Asian J. Control 1, 14–24 (1999)

    Article  Google Scholar 

  58. T. Raff, C. Ebenbauer, F. Allgoewer, Nonlinear Model Predictive Control: Passivity-based Approach (Springer, New York, 2007)

    Google Scholar 

  59. A. Tahirovic, G. Magnani, P. Rocco, Mobile robot navigation using passivity-based MPC. in Proceedings of the IEEE/ASME International Conference on Advanced Intelligent, Mechatronics, pp. 248–488, July 2010

    Google Scholar 

  60. A. Tahirovic, G. Magnani, General framework for mobile robot navigation using passivity-based MPC. IEEE Trans. Autom. Control 56(1), 184–190 (2011)

    Google Scholar 

  61. A. Tahirovic, G. Magnani, Passivity-based model predictive control for mobile robot navigation planning in rough terrains. in Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct 2010

    Google Scholar 

  62. C.A. Desoer, M. Vidyasagar, Feedback Systems: Input-Output Properties (Academic Press, New York, 1975)

    MATH  Google Scholar 

  63. J.C. Willems, Dissipative dynamical systems part 1.: general theory. Arch. Ration. Mech. Anal. 45(5), 321–351 (1972)

    Article  MathSciNet  MATH  Google Scholar 

  64. J.C. Willems, Dissipative dynamical systems part 2.: linear systems with quadratic supply rates. Arch. Ration. Mech. Anal. 45(5), 352–393 (1972)

    Article  MathSciNet  MATH  Google Scholar 

  65. D. Youla, L. Castriota, H. Carlin, Bounded real scattering matrices and the foundations of linear passive network theory. IRE Trans. Circ. Theory 6(1), 102–124 (1959)

    Article  Google Scholar 

  66. P. Moylan, B. Anderson, Nonlinear regulator theory and an inverse optimal control problem. IEEE Trans. Autom. Control 18(5), 460–465 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  67. D. Hill, P. Moylan, The stability of nonlinear dissipative systems. IEEE Trans. Autom. Control 21(5), 708–711 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  68. C.I. Byrnes, A. Isidori, J. Willems, Passivity, feedback equivalence, and the global stabilization of minimum phase nonlinear systems. IEEE Trans. Autom. Control 36(11), 1228–1240 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  69. A. Ailon, R. Ortega, An observer-based set-point controller for robot manipulators with flexible joints. Syst. Control Lett. 21(4), 329–335 (1993)

    Google Scholar 

  70. H.K. Khalil, Nonlinear Systems, 3rd edn. (Prentice Hall, Upper Saddle River, 2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Adnan Tahirovic .

Rights and permissions

Reprints and permissions

Copyright information

© 2013 The Author(s)

About this chapter

Cite this chapter

Tahirovic, A., Magnani, G. (2013). Introduction. In: Passivity-Based Model Predictive Control for Mobile Vehicle Motion Planning. SpringerBriefs in Electrical and Computer Engineering(). Springer, London. https://doi.org/10.1007/978-1-4471-5049-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-5049-7_1

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5048-0

  • Online ISBN: 978-1-4471-5049-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics