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Car-Like Mobile Robot Navigation: A Survey

  • Sotirios SpanogianopoulosEmail author
  • Konstantinos Sirlantzis
Chapter
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Part of the Studies in Computational Intelligence book series (SCI, volume 627)

Abstract

Car-like mobile robot navigation has been an active and challenging field both in academic research an in industry over the last few decades, and it has opened the way to build and test (recently) autonomously driven robotic cars which can negotiate the complexity and uncertainties introduced by real urban and suburban environments. In this chapter, we review the basic principles and discuss the corresponding categories in which current methods and associated algorithms for car-like vehicle autonomous navigation belong. They are used especially for outdoor activities and they have to be able to account for the constraints imposed by the non-holonomic type of movement allowable for car-like mobile robots. In addition, we present a number of projects from various application areas in the industry that are using these technologies. Our review starts with a description of a very popular and successful family of algorithms, namely the Rapidly-exploring Random Tree (RRT) planning method. After discussing the great variety and modifications proposed for the basic RRT algorithm, we turn our focus to versions which can address highly dynamic environments, especially those which become increasingly uncertain due to limited accuracy of the sensors used. We, subsequently, explore methods which use Fuzzy Logic to address the uncertainty and methods which consider navigation solutions within the holistic approach of a Simultaneous Localization and Mapping (SLAM) framework. Finally, we conclude with some remarks and thoughts about the current state of research and possible future developments.

Keywords

Rapidly-exploring random trees (RRT) Simultaneous localization and mapping (SLAM) Sensor-based methods Fuzzy logic Path planning 

References

  1. Balakirsky, S., Dimitrov, D.: Single-query, bi-directional, lazy roadmap planner applied to car-like robots. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 5015–5020 (2010)Google Scholar
  2. Baturone, I., Gersnoviez, A.: A simple neuro-fuzzy controller for car-like robot navigation avoiding obstacles. In: IEEE FUZZ, pp. 1–6 (2007)Google Scholar
  3. Burns, B., Brock, O.: Single-query motion planning with utility-guided random trees. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3307–3312 (2007)Google Scholar
  4. Cheein, F.A.A., Carelli, R., De la Cruz, C., Bastos-Filho, T.F.: SLAM-based turning strategy in restricted environments for car-like mobile robots. In: International Conference on Industrial Technology (ICIT), pp. 602–607 (2010)Google Scholar
  5. Delsart, V., Fraichard, T.: Navigating dynamic environments using trajectory deformation. In: IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 226–233 (2008)Google Scholar
  6. Devaurs, D., Simeon, T., Cortes, J.: Parallelizing RRT on large-scale distributed-memory architectures. IEEE Trans. Robot. Autom. 29, 571–579 (2013)CrossRefGoogle Scholar
  7. El-Khatib, M.M., Hamilton, D.J.: A layered fuzzy controller for nonholonomic car-like robot motion planning. In: IEEE International Conference on Mechatronics, pp. 194–198 (2006)Google Scholar
  8. Espinoza, J.L., Sánchez, A., Osorio, M.A.: Exploring unknown environments with RRT-based strategies. In: International Conference on Artificial Intelligence, pp. 1150–1159 (2006)Google Scholar
  9. Fulgernzi, C., Spalanzani, A., Laugier, C.: Dynamic obstacle avoidance in uncertain environment combining PVOs and occupancy grid. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1610–1616 (2007)Google Scholar
  10. Fulgenzi, C., Tay, C., Spalanzani, A., Laugier, C.: Probabilistic navigation in dynamic environment using rapidly-exploring random trees and Gaussian processes. In: Intelligent Robots and Systems (IROS), pp. 1056–1062 (2008)Google Scholar
  11. Gall, R., Troster, F., Mogan, G.: On the development of an experimental car-like mobile robot. In: International Conference on Optimization of Electrical and Electronic Equipment (OPTIM), pp. 734–739 (2010)Google Scholar
  12. Garrido, S., Blanco, D., Moreno, L., Martın, F.: Improving RRT motion trajectories using VFM. In: IEEE International Conference on Mechatronics (ICM), pp. 1–6 (2009)Google Scholar
  13. Garrido, S., Moreno, L., Blanco, D., Martın, F.: Smooth path planning for non-holonomic robots using fast marching. In: Int. J. Robot. Autom. 154–176 (2011)Google Scholar
  14. Grady, D.K., Moll, M., Hegde, C., Sankaranarayanan, A.C., Baraniuk, R.G., Kavraki, L.E.: Multi-objective sensor-based replanning for a car-like robot. In: International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 1–6 (2012)Google Scholar
  15. Ho, J.W., Wright, M., Das, S.K.: Distributed detection of mobile malicious node attacks in wireless sensor networks. Ad Hoc Netw. 10(3), 512–523 (2012)CrossRefGoogle Scholar
  16. Hwang, C.-L., Chang, L.-J.: Internet-based smart-space navigation of a car-like wheeled robot using fuzzy-neural adaptive control. IEEE Trans. Fuzzy Syst. 16(5), 1271–1284 (2008)CrossRefGoogle Scholar
  17. Hwang, C.-L., Shih, C.Y.: A distributed active-vision network-space approach for the navigation of a car-like wheeled robot. IEEE Trans. Industr. Electron. 56(3), 846–855 (2009)CrossRefGoogle Scholar
  18. Jaillet, L., Yershova, A., LaValle, S.M., Simeon, T.: Adaptive tuning of the sampling domain for dynamic-domain RRTs. In: IEEE International Conference on Intelligent Robots and Systems, pp. 2851–2856 (2005)Google Scholar
  19. Jaillet, L., Cortés, J., Siméon, T.: Transition-based RRT for path planning in continuous cost spaces. In: IEEE International Conference on Robots and Systems, pp. 2646–2652 (2012)Google Scholar
  20. Karaman, S., Frazzoli, E.: Sampling-based optimal motion planning for non-holonomic dynamical systems. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 5041–5047 (2013)Google Scholar
  21. Kim, J., Esposito, J.M., Kumar, V.: An RRT-based algorithm for testing and validating multi-robot controllers. In: Conference on Robotics Science and Systems, pp. 249–256 (2005)Google Scholar
  22. Kuwata, Y., Fiore, G.A., Teo, J., Frazzoli, E., How, J.P.: Motion planning for urban driving using RRT. In: International Conference on Intelligent Robots and Systems (IROS), pp. 1681–1686 (2008)Google Scholar
  23. Lategahn, H., Geiger, A., Kitt, B.: Visual SLAM for autonomous ground vehicles. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1732–1737 (2011)Google Scholar
  24. Levinson, J., Askeland, J., Becker, J., Dolson, J., Held, D., Kammel, S., Thrun, S., et al.: Towards fully autonomous driving: systems and algorithms. In: IEEE Intelligent Vehicles Symposium (IV), pp. 163–168 (2011) Google Scholar
  25. Maddern, W., Vidas, S.: Towards robust night and day place recognition using visible and thermal imaging. RSS 2012: beyond laser and vision: alternative sensing techniques for robotic perception (2012)Google Scholar
  26. Mathias, M., Timofte, R., Benenson, R., Van Gool, L.: Traffic sign recognition—how far are we from the solution? In: IEEE International Joint Conference on Neural Networks (IJCNN), August 2013, pp. 1–8Google Scholar
  27. Niclass, C., Soga, M., Matsubara, H., Kato, S., Kagami, M.: A 100-m range 10-frame/s 340 96-pixel time-of-flight depth sensor in 0.18-CMOS. IEEE J. Solid-State Circ. 48(2), 559–572 (2013)Google Scholar
  28. Pepy, R., Lambert, A.: Safe path planning in an uncertain-configuration space using RRT. In: Intelligent Robots and Systems (IROS), pp. 5376–5381 (2006)Google Scholar
  29. Pepy, R., Lambert, A., Mounier, H.: Path planning using a dynamic vehicle model. In: ICTTA, pp. 781-786 (2006)Google Scholar
  30. Petridis, V., Zikos, N.: L-SLAM: reduced dimensionality FastSLAM algorithms. In: IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1–7 (2010)Google Scholar
  31. Petti, S., Fraichard, T.: Safe navigation of a car-like robot in a dynamic environment. In: European Conference on Mobile Robots (2005)Google Scholar
  32. Phillips, J.M., C.S. Draper Laboratories: Guided expansive spaces trees: a search strategy for motion- and cost-constrained state spaces. In: International Conference on Robotics and Automation (ICRA), vol. 4, pp. 3968–3973 (2004)Google Scholar
  33. Pradalier, C., Hermosillo, J., Koike, C., Braillon, C., Bessihre, P., Laugier, C.: An autonomous car-like robot navigating safely among pedestrians. In: IEEE International Conference on Robotics and Automation (ICRA), vol. 2, pp. 1945–1950 (2004)Google Scholar
  34. Pradalier, C., Hermosillo, J., Koike, C., Braillon, C., Bessière, P., Laugier, C.: The CyCab: a car-like robot navigating autonomously and safely among pedestrians. In: Robotics and Autonomous Systems, pp. 51–67 (2005)Google Scholar
  35. Quan, Y., Lee, J.Y., Changsoo, H.: Sensor-based navigation algorithm for car-like robot to generate completed GVG. In: International Conference on Control, Automation and Systems (ICCAS), pp. 1442–1447 (2011) Google Scholar
  36. Rebai, K., Azouaoui, O., Benmami, M., Larabi, A.: Car-like robot navigation at high speed. In: Robotics and Biomimetics, pp. 2053–2057 (2007)Google Scholar
  37. Rezaei, S., Guivant, J.E., Nebot, E.M.: Car-like robot path following in large unstructured environments. In: IEEE IROS, pp. 2468–2473 (2004)Google Scholar
  38. Rodriguez, S., Tang, X., Lien, J.-M., Amato, N.M.: An obstacle- based rapidly-exploring random tree. In: International Conference on Robotics and Automation, pp. 895–900 (2006)Google Scholar
  39. Tahirovic, A., Magnani, G.: A roughness-based RRT for mobile robot navigation planning. In: IFAC World Congress, vol. 18, pp. 5944–5949 (2001)Google Scholar
  40. Whaiduzzaman, M., Sookhak, M., Gani, A., Buyya, R.: A survey on vehicular cloud computing. J. Netw. Comput. Appl. 40, 325–344 (2014)CrossRefGoogle Scholar
  41. Yershova, A., Jaillet, L., Simeon, T., LaValle, S.M.: Dynamic-domain RRTs: efficient exploration by controlling the sampling domain. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3856–3861 (2005)Google Scholar
  42. Ziegler, J., Werling, M.: Navigating car-like robots in unstructured environments using an obstacle sensitive cost function. In: IEEE Intelligent Vehicles Symposium, pp. 787–791 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Sotirios Spanogianopoulos
    • 1
    Email author
  • Konstantinos Sirlantzis
    • 1
  1. 1.School of Engineering and Digital ArtsUniversity of KentCanterburyUK

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