Development of the Searching Algorithm with Complexity Environment for Mobile Robots

Abstract:

The article programs the shortest path searching problems of the mobile robot in the complexity unknown environment, and uses the mobile robot to present the movement scenario from the start point to the target point in a collision-free space. The complexity environment contains variety obstacles, such as road, tree, river, gravel, grass, highway and unknown obstacle. We set the relative dangerous grade for variety obstacles. The mobile robot searches the target point to locate the positions of unknown obstacles, and avoids these obstacles moving in the motion platform. We develop the user interface to help users filling out the positions of the mobile robot and the obstacles on the supervised computer, such the initial point of the mobile robot, the start point and the target point. The supervised computer programs the motion paths of the mobile robot according to A* searching algorithm, flood-fill algorithm and 2-op exchange algorithm The simulation results present the proposed algorithms that program the shortest motion paths from the initial point approach to the target point for the mobile robot. The supervised computer controls the mobile robot that follows the programmed motion path moving to the target point in the complexity environment via wireless radio frequency (RF) interface.

• Periodical: Applied Mechanics and Materials (Volumes 284-287)
• Pages: 1826-1830
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.284-287.1826
• Online since: January 2013
• Authors: Yung Chin Lin, Kuo Lan Su, Chih Hung Chang
• Keywords: 2-Op Exchange Algorithm, A* Searching Algorithm, Flood Fill Algorithm, Multiple Mobile Robots, Wireless RF Interface

References

[1] A.K. Kulatunga, D. k. Liu and G. Dissanayake, Ant colony optimization based simultaneous task allocation and path planning of autonomous vehicles, IEEE Conf. on Cybernetics and Intelligent System (2006), p.1.

DOI: 10.1109/iccis.2006.252349

[2] V. Mizumoto, J. Yu and K. Shibayama, 3D path planning for mobile robots using simulated annealing neural network, International Journal of Innovative Computing, Information and Control, Vol. 6, No. 7 (2010), p.2885.

[3] C.F. Juang and C.H. Hsu, Reinforcement ant optimized fuzzy controller for mobile-robot wall-following control, IEEE Trans. on Industrial Electronics, Vol. 56, No. 10 (2009), p.3931.

DOI: 10.1109/tie.2009.2017557

[4] K. Sugihara and J. Smith, Genetic algorithm for adaptive motion planning of an autonomous mobile robot, IEEE International Symposium on Computational Intelligence in Robotics and Automation (1997), p.138.

DOI: 10.1109/cira.1997.613850

[5] X.F. Dai, X.M. Ning and Y. Shi, A novel path planning algorithm for mobile robots based on cloud model, ICIC Express Letters, Vol. 3, N0. 4(A) (2009), p.877.

[6] W. Yan and W. Wu, Data structure, Tsinghua University Press, Beijing (2004), p.156.

[7] K.L. Su, C.Y. Chung, Y.L. Liao and J.H. Guo, A* Searching Algorithm Based Path Planning of Mobile Robots, Innovative Computing, Information and Control – Express Letters, Part B: Applications, Vol. 2, No. 1 (2011), p.273.