Neural Based Autonomous Navigation of Wheeled Mobile Robots

• Autorzy: Al-Sagban M. , Dhaouadi R.

Identyfikatory

DOI

10.14313/JAMRIS_2-2016/17

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

reactive navigation; obstacle avoidance; autonomous ground robots; recurrent neural network

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2016
• Tom: Vol. 10, No. 2
• Strony: 64--72
• Opis fizyczny: Bibliogr. 11 poz., rys.

Twórcy

autor

Al-Sagban M.

• American University of Sharjah, UAE

autor

Dhaouadi R.

• 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.
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• [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. P􀏐luger, “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.