Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
This paper presents a novel reactive navigation algorithm for wheeled mobile robots under non-holonomic constraints and in unknown environments. Two techniques are proposed: a geometrical based technique and a neural network based technique. The mobile robot travels to a pre-defined goal position safely and efficiently without any prior map of the environment by modulating its steering angle and turning radius. The dimensions and shape of the robot are incorporated to determine the set of all possible collision-free steering angles. The algorithm then selects the best steering angle candidate. In the geometrical navigation technique, a safe turning radius is computed based on an equation derived from the geometry of the problem. On the other hand, the neural-based technique aims to generate an optimized trajectory by using a user-defined objective function which minimizes the traveled distance to the goal position while avoiding obstacles. The experimental results demonstrate that the algorithms are capable of driving the robot safely across a variety of indoor environments.
Rocznik
Tom
Strony
64--72
Opis fizyczny
Bibliogr. 11 poz., rys.
Twórcy
autor
- American University of Sharjah, UAE
autor
- American University of Sharjah, UAE
Bibliografia
- [1] M. Al-Sagban and R. Dhaouadi, “Neural-based navigation of a differential-drive mobile robot”, 12th International Conference on Control Automation Robotics Vision, 2012, 353–358.
- [2] M. Al-Sagban. “Autonomous robot navigation based on recurrent neural networks”. Master’s thesis, American University of Sharjah, 2012.
- [3] V. Castro, J. Neira, C. Rueda, J. Villamizar, and L. Angel, “Autonomous navigation strategies for mobile robots using a probabilistic neural network (pnn)”, 33rd Annual Conference of the IEEE Industrial Electronics Society, 2007, 2795–2800.
- [4] X. Chen and Y. Li, ““smooth formation navigation of multiple mobile robots for avoiding moving obstacles””, International Journal of Control, Automation, and Systems, vol. 4, no. 4, 2006, 466–479.
- [5] W. Chung, L.-C. Fu, and S.-H. Hsu. “Motion control”. In: “Springer Handbook of Robotics”, 133–159. 2008.
- [6] D. Janglová, “Neural networks in mobile robot motion”, Inernational Journal of Advanced Robotic Systems, vol. 1, no. 1, 2004, 15–22.
- [7] L. Kavraki, P. Svestka, J.-C. Latombe, and M. Overmars, “Probabilistic roadmaps for path planning in high-dimensional confiiguration spaces”, IEEE Transactions on Robotics and Automation, vol. 12, no. 4, 1996, 566 –580.
- [8] O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots”, International Journal of Robotics Research, vol. 5, no. 1, 1986, 90–98.
- [9] N. D. Munoz, J. A. Valencia, and N. Londono, “Evaluation of navigation of an autonomous mobile robot”, Proceedings of the 2007 Performance Metrics for Intelligent Systems Workshop, Maryland USA, 2007, 15–21.
- [10] H. T. Trieu, H. T. Nguyen, and K. Willey, “Advanced obstacle avoidance for a laser based wheelchair using optimised bayesian neural networks”, 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008, 3463 –3466.
- [11] J. Yen and N. Pluger, “A fuzzy logic based extension to payton and rosenblatt’s command fusion method for mobile robot navigation”, IEEE Transactions on Systems, Man and Cybernetics, vol. 25, no. 6, 1995, 971 –978.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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