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An Autonomous Vision System Based Sensor-Motor Coordination for Mobile Robot Navigation in an Outdoor Environment

  • Xiaochun WangEmail author
  • Xiali Wang
  • Don Mitchell Wilkes
Chapter

Abstract

Autonomous mobile robots are intelligent machines capable of performing tasks in the real world without explicit human control for extended periods of time. For a mobile robot to navigate from a starting position to a goal position within its environment, a path should be identified to generate an obstacle-free trajectory between the starting point and the goal point. Based on a vision-based autonomous percept acquisition system, this chapter discusses the basic ideas of the proposed autonomous navigation strategy of mobile robots by using vision as the sensing mechanism in achieving the desired objectives. The success of the proposed computer vision-based robot system is demonstrated by its application to a simple sensor-motor coordinated navigation task.

Keywords

Computer vision Pattern recognition Clustering Classification tree Machine learning Sensor-motor coordination Working memory toolkit 

References

  1. Akbarimajd, A., & Hassanzadeh, A. (2011). A novel cellular automata based real time path planning method for mobile robots. Internatioal Journal of Engineering Research and Applications.Google Scholar
  2. Akbarimajd, A., & Lucas, C. (2006). A new architecture to execute CAs-based path-planning algorithm in mobile robots. In Proceedings of the IEEE International Conference on Mechatronics (pp. 478–482).Google Scholar
  3. Amato, N. M., & Wu, Y. (1996). A randomized roadmap method for path and manipulation planning. In Proceedings of IEEE International Conference on Robotics and Automation (Vol. 1, p. 7).Google Scholar
  4. Amato, N. M., Bayazit, O. B., Dale, L. K., Jones, C., & Vallejo, D. (1998). OBPRM: An obstacle-based PRM for 3D workspaces. In Proceedings of the Third Workshop on the Algorithmic Foundations of Robotics on Robotics: The Algorithmic Perspective (pp. 155–168).Google Scholar
  5. Arkin, R. C. (1998). Behavior-based robotics. MIT Press.Google Scholar
  6. Barraquand, J., Langlois, B., & Latombe, J.-C. (1992). Numerical potential field techniques for robot path planning. IEEE Transactions on Systems, Man, and Cybernetics, 22(2), 224–241.MathSciNetCrossRefGoogle Scholar
  7. Behring, C., Bracho, M., Castro, M., & Moreno, J. A. (2000). An algorithm for robot path planning with cellular automata. In Proceedings of the 4th International Conference on Cellular Automata for Research and Industry (pp. 11–19).Google Scholar
  8. Bohlin, R., & Kavraki, L. E. (2000). Path planning using lazy PRM. In Proceedings of IEEE International Conference on Robotics and Automation (Vol. 1, pp. 521–528). San Francisco, CA.Google Scholar
  9. Boor, V., Overmars, M. H., & van der Stappen, A. F. (1999). The Gaussian sampling strategy for probabilistic roadmap planners. In Proceedings of 1999 IEEE International Conference on Robotics and Automation (pp. 1018–1023).Google Scholar
  10. Branicky, M. S., LaValle, S. M., Olson, K., & Yang, L. (2001). Quasi-randomized path planning. In Proceedings of the IEEE International Conference on Robotics and Automation (pp. 1481–1487).Google Scholar
  11. Buhmann, J. M., Lange, T., & Ramacher, U. (2005). Image segmentation by networks of spiking neurons. Neural Computation, 17, 1010–1031.CrossRefGoogle Scholar
  12. Butler, A. B., & Hodos, W. (1996). Comparative vertebrate neuroanatomy: Evolution and adaptation. Wiley.Google Scholar
  13. Frazzoli, E., Dahleh, M. A., & Feron, E. (2002). Real-time motion planning for agile autonomous vehicles. Journal of Guidance, Control and Dynamics, 25, 116–129.CrossRefGoogle Scholar
  14. Fukuda, T., Michelini, R., Potkonjak, V., Tzafestas, S., Valavanis, K., & Vukobratovic, M. (2001). How far away is “artificial man”? IEEE Robotics & Automation Magazine, 8(1), 66–73.Google Scholar
  15. Hart, P., Nilsson, N., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100–107.CrossRefGoogle Scholar
  16. Hunter, J. (2005). Human motion segmentation and object recognition using fuzzy rules. In Proceedings of 14th Annual IEEE International Workshop on Robot and Human Interactive Communication (RO-MAN’05) (pp. 210–216), Nashville, TN, August 13–15.Google Scholar
  17. Ioannidis, K., Sirakoulis, G. C., & Andreadis, I. (2008). A cellular automaton collision-free path planner suitable for cooperative robots. In Panhellenic Conference on Informatics. IEEE Computer Society.Google Scholar
  18. Ioannidis, K., Sirakoulis, G., & Andreadis, I. (2011). Cellular ants: A method to create collision free trajectories for a cooperative robot team. Robotics and Autonomous Systems.Google Scholar
  19. Kavraki, L. E. (1995). Random networks in configuration space for fast path planning. Stanford, CA: Stanford University.Google Scholar
  20. Kavraki, L., & Latombe, J. C. (1994). Randomized preprocessing of configuration space for fast path planning. In Proceedings of IEEE International Conference on Robotics Automation (ICRA’94) (p. 7).Google Scholar
  21. Klein, R., Langetepe, E., & Nilforoushan, Z. (2009). Abstract voronoi diagrams revisited. Computational Geometry: Theory and Applications, 42(9), 885–902.MathSciNetCrossRefGoogle Scholar
  22. Lavalle, S. (1998). Rapidly-exploring random trees: A new tool for path planning (Research Report 9811). Department of Computer Science, Iowa State University.Google Scholar
  23. LaValle, S. M., & Kuffner, J. J., Jr. (2001). Randomized kinodynamic planning. International Journal of Robotic Research, 20, 378–400.CrossRefGoogle Scholar
  24. Lee, D. T., & Preparata, F. P. (1984). Euclidean shortest paths in the presence of rectilinear barriers. Networks, 14, 93–410.MathSciNetCrossRefGoogle Scholar
  25. Liu, F., Liang, S., Xian, X., & Bi, H. (2012). Optimal path planning for mobile robot in consideration of road attributes. ICIC Express Letters, 6, 281–287.Google Scholar
  26. Lumelsky, V. J., & Stepanov, A. A. (1987). Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape. Algorithmica, 2, 403–430.MathSciNetCrossRefGoogle Scholar
  27. Marchese, F. (2005). A reactive planner for mobile robots with generic shapes and kinematics on variable terrains. In Proceedings of International Conference on Advanced Robotics (ICAR’05) (pp. 23–30).Google Scholar
  28. Mitchell, M. (1996). Computation in cellular automata: A selected review. In Non-standard computation (pp. 385–390).Google Scholar
  29. Mitchell, J. S. B., & Papadimitriou, C. H. (1991). The weighted region problem: Finding shortest paths through a weighted planar subdivision. Journal of the ACM, 38(1), 18–73.MathSciNetCrossRefGoogle Scholar
  30. Nehmzow, U. (2003). Mobile robotics: A practical introduction. London, New York: Springer.Google Scholar
  31. Oliveira, G., Martins, L. G. A., de Carvalho, L. B., & Fynn, E. (2009). Some investigations about synchronization and density classification tasks in one-dimensional and two-dimensional cellular automata rule spaces. Electronic Notes in Theoretical Computer Science, 252, 121–142.MathSciNetCrossRefGoogle Scholar
  32. Ramer, C., Reitelshofer, S., & Franke, J. (2013). A robot motion planner for 6-DOF industrial robots based on the cell decomposition of the workspace. In Proceedings of the 2013 44th International Symposium on Robotics (ISR’13) (pp. 1–4).Google Scholar
  33. Rosenberg, A. (2007). Cellular automata. In Parallel and distributed processing and applications (pp. 78–90).CrossRefGoogle Scholar
  34. Shu, C., & Buxton, H. (1995). Parallel path planning on the distributed array processor. Parallel Computing, 21(11), 1749–1767.CrossRefGoogle Scholar
  35. Soofiyani, F. R., Rahmani, A. M., & Mohsenzadeh, M. (2010). A straight moving path planner for mobile robots in static environments using cellular automata. In Proceedings of International Conference on Computational Intelligence, Communication Systems and Networks (pp. 67–71).Google Scholar
  36. Sperry, R. W. (1952). Neurology and the mind-brain problem. American Scientist, 40, 291–312.Google Scholar
  37. Stentz, A. (1997). Optimal and efficient path planning for partially known environments. In M. H. Hebert, C. Thorpe, & A. Stentz (Eds.), Intelligent unmanned ground vehicles (pp. 203–220). US: Springer.CrossRefGoogle Scholar
  38. Striedter, G. (2004). Principles of brain evolution. Sinauer Associates.Google Scholar
  39. Tzionas, P., Thanailakis, A., & Tsalides, P. (1997). Collision-free path planning for a diamond shaped robot using two-dimensional CA. IEEE Transactions on Robotics and Automation, 13(2), 237–250.CrossRefGoogle Scholar
  40. Yu, J., Du, H., & Zhou, L. (2013). Research about local path planning of moving robot based on improved artificial potential field. In Proceedings of Chinese Control and Decision Conference (CCDC’13) (pp. 2861–2865).Google Scholar
  41. Zhang, Y., Fattahi, N., & Li, W. (2013). Probabilistic roadmap with self-learning for path planning of a mobile robot in a dynamic and unstructured environment. In Proceedings of the IEEE International Conference on Mechatronics & Automation (ICMA’13) (pp. 1074–1079).Google Scholar

Copyright information

© Xi'an Jiaotong University Press 2020

Authors and Affiliations

  • Xiaochun Wang
    • 1
    Email author
  • Xiali Wang
    • 2
  • Don Mitchell Wilkes
    • 3
  1. 1.School of Software EngineeringXi’an Jiaotong UniversityXi’anChina
  2. 2.School of Information EngineeringChang’an UniversityXi’anChina
  3. 3.Department of Electrical Engineering and Computer ScienceVanderbilt UniversityNashvilleUSA

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