Abstract
Autonomous behavior agent includes the robot most behaviors which are designed using hierarchical control method to guarantee their real time performance for real time navigation in response to different situation perceived. The process of robot real time navigation based on the autonomous behavior agent mainly includes three behaviors. The sensing behavior translates the configuration space that the robot and obstacles exist in into 2D Cartesian Grid by Quadtree method. The path planning behavior designs the sub-goals given the global map, start and goal points by improved D* Lite Algorithm. And the obstacle avoidance behaivor replans the path between two adjacent sub-goals when the environment changes. It is able to replan faster than planning from scratch since it modifies its previous search results locally and enables robots adapt to the dynamic environment. The simulation results that are reported show that the mobile robot navigation method is efficient and feasible.
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References
Brooks, R.A.: A Robust Layered Control System for A Mobile Robot. IEEE Journal of Robotics And Automation, RA-2(1) (March 1986)
Samer, A.M., Ahmad, A.M.: Constrained Motion Control Using Vector Potential Fields. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans 30(3) (2000)
Khatib, 0.: A Unified Approach for Motion and Force Control of Robot Manipulators: The Operational Space Formulation. IEEE Journal of Robotics and Automation RA. 3(1) (February 1987)
Choset, H., Walker, S.: Sensor-Based Exploration: Incremental Construction of the Hierarchical Generalized Voronoi Graph. The International Journal of Robotics Research 19, 126–148 (2000)
Latombe, J-C., Barraquand, J.: Robot Motion Planning: A Distributed Presentation Approach. International Journal of Robotics Research 10, 628–649 (1991)
Jacobs, R., Canny, J.: Planning Smooth Paths for Mobile Robots. In: Proc. IEEE Conference on Robotics and Automation, pp. 2–7. IEEE Press, Los Alamitos (1989)
Russell, S., Norvig, P.: Artificial Intelligence, a Modern Approach. Prentice-Hall, Englewood Cliffs (1995)
Stentz, A.: Optimal and efficient path planning for partially-known environments. In: Proc. Int. Conf. Robot. Autom., pp. 3310–3317 (1994)
Stentz, A.: The Focussed D* Algorithm for Real Time Replanning. In: Proc. Int. Joint Conf. Artificial Intelligence, pp. 1652–1659 (1995)
Pearl, J.: Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley, Reading, MA (1985)
Hebert, M., et al.: Experiments with Driving Modes for Urban Robots. In: Proc. SPIE Mobile Robots, pp. 140–149 (1999)
Likhachev, M.: Anytime Dynamic A*: An Anytime, Replanning Algorithm. In: Proc. Int. Conf. Automated Planning and Scheduling (2005)
Koening, S., Likhachev, M.: Incremental A*, Advances in Neural Information Processing Systems 14. MIT Press, Cambridge, MA (2002)
Likhachev, M.: Search-based Planning for Large Dynamic Environments. PhD thesis, CMU (2005)
Koenig, S.: Fast Replanning for Navigation in Unknown Terrain. IEEE Transactions on Robotics 21(3) (June 2005)
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Xu, L., Zhang, L., Chen, Y. (2007). Real-Time Navigation for a Mobile Robot Based on the Autonomous Behavior Agent. In: Gervasi, O., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2007. ICCSA 2007. Lecture Notes in Computer Science, vol 4707. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74484-9_7
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DOI: https://doi.org/10.1007/978-3-540-74484-9_7
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