Abstract
Mobile robot can follow the planned path using a waypoint following guidance scheme. As this type of guidance scheme only uses the position of waypoints to navigate the path, the waypoint following is relatively simple and efficient to implement. However, it is non-trivial to determine the number and size of waypoints, which heavily affect the performance of robot. Thus, we tackle the problem of finding the optimal number and size of waypoints in this paper. For this optimization problem, we use genetic algorithm, where the effectiveness of the proposed method is verified in MATLAB simulation. The proposed method shows that mobile robot effectively navigates the planned path and successfully reaches the destination with the minimum path following error and travel time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ibrahim, M.T.S., Ragavan, S.V., Ponnambalam, S.G.: Way point based deliberative path planner for navigation. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009, pp. 881–886 (2009)
Pradhan, S.K., Parhi, D.R., Panda, A.K., Behera, R.K.: Potential field method to navigate several mobile robots. Applied Intelligence 25(3), 321–333 (2006)
Mulvaney, D., Wang, Y., Sillitoe, I.: Waypoint-based Mobile Robot Navigation. In: The 6th World Congress on Intelligent Control and Automation, vol. 2, pp. 9063–9067 (2006)
Shair, S., Chandler, J.H., Gonzalez-Villela, V.J., Parkin, R.M., Jackson, M.R.: The use of aerial images and GPS for mobile robot waypoint navigation. IEEE/ASME Trans. Mechatronics 13(6), 692–699 (2008)
Boucher, P., Cohen, P.: A smoothness-preserving waypoints follower for mobile platforms. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 471–476 (2010)
Shima, T., Rasmussen, S. (eds.): UAV Cooperative Decision and Control: Challenges and Practical Approaches, vol. 18. SIAM (2009)
Gen, M., Cheng, R.: Genetic algorithms and engineering optimization, vol. 7. John Wiley & Sons (2000)
Konak, A., Coit, D.W., Smith, A.E.: Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety 91(9), 992–1007 (2006)
Khalil, H.K.: Nonlinear System, 2nd edn. Prentice Hall (1996)
Kim, D.H., Kim, J.H.: A real-time limit-cycle navigation method for fast mobile robots and its application to robot soccer. Robotics and Autonomous Systems 42, 17–30 (2003)
Lim, Y.W., Kim, J.W., Nam, S.Y., Kim, D.H.: Local-path planning using the limit-cycle navigation method with the edge detection method. In: ICEIC: International Conference on Electronics, Informations and Communications, pp. 232–234 (2010)
Davidor, Y.: Genetic Algorithms and Robotics: a heuristic strategy for optimization. World Scientific Publishing Singapore (1991)
Min, B.C., Lewis, J., Matson, E.T., Smith, A.H.: Heuristic optimization techniques for self-orientation of directional antennas in long-distance point-to-point broadband networks. Ad Hoc Networks (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Yoon, JS., Min, BC., Shin, SO., Jo, WS., Kim, DH. (2014). GA-Based Optimal Waypoint Design for Improved Path Following of Mobile Robot. In: Kim, JH., Matson, E., Myung, H., Xu, P., Karray, F. (eds) Robot Intelligence Technology and Applications 2. Advances in Intelligent Systems and Computing, vol 274. Springer, Cham. https://doi.org/10.1007/978-3-319-05582-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-05582-4_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05581-7
Online ISBN: 978-3-319-05582-4
eBook Packages: EngineeringEngineering (R0)