Abstract
In this article, the atmospheric motion-based inspired wind-driven optimization (WDO) algorithm is implemented to minimize the traveling path length of a four-wheeled ground robot (FWGR) in different stationary and non-stationary environmental conditions. This optimization algorithm works on the principle of atmospheric motion of very small air particles, which revolves over the multi-dimensional search area. In the present study, WDO algorithm is employed to search a minimal or near-minimal steering angle for the (FWGR); this steering angle minimizes the path length during motion, orientation, and collision avoidance. The objective function for the WDO algorithm has been created for two reasons: for obstacle avoidance and traveling path optimization in the environments from the source point to the endpoint. Simulation results demonstrate that the FWGR covers a shorter path length using WDO algorithm as compared to the path length obtained by the FWGR using particle swarm optimization (PSO) algorithm and genetic algorithm (GA).
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
References
Patel BK, Pandey A, Jagadeesh A, Parhi DR (2018) Path planning in the uncertain environment by using firefly algorithm. Def Technol 14(6):691–701
Chen X, Kong Y, Fang X, Wu Q (2013) A fast two–stage ACO algorithm for robotic path planning. Neural Comput Appl 22(2):313–319
Pandey A, Parhi DR (2016) Autonomous mobile robot navigation in the cluttered environment using hybrid Takagi-Sugeno fuzzy model and simulated annealing algorithm controller. World J Eng 13(5):431–440
Deepak BBVL, Parhi DR, Raju BMVA (2014) Advance particle swarm optimization-based navigational controller for a mobile robot. Arabian J Sci Eng 39(8):6477–6487
Jianguo W, Yilong Z, Linlin X (2010) Adaptive genetic algorithm enhancements for path planning of mobile robots. In: International conference on measuring technology and mechatronics automation. IEEE, Changsha City, China, pp 416–419
Bhandari AK, Singh VK, Kumar A, Singh GK (2014) Cuckoo search algorithm and wind-driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Syst Appl 41(7):3538–3560
Bayraktar Z, Turpin JP, Werner DH (2011) Nature-Inspired optimization of high-impedance metasurfaces with ultrasmall interwoven unit cells. Antenn Wirel Propag Lett 10:1563–1566
Mac TT, Copot C, Tran DT, De Keyser R (2017) A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization. Appl Soft Comput 59:68–76
Pandey A, Kumar S, Pandey KK, Parhi DR (2016) Mobile robot navigation in unknown static environments using ANFIS controller. Persp Sci 8:421–423
Singh NH, Thongam K (2019) Neural network-based approaches for mobile robot navigation in static and moving obstacles environments. Intel Serv Robot 12(1):55–67
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bej, N., Pandey, A., Kashyap, A.K., Parhi, D.R. (2020). Optimum Navigation of Four-Wheeled Ground Robot in Stationary and Non-stationary Environments Using Wind-Driven Optimization Algorithm. In: Deepak, B., Parhi, D., Jena, P. (eds) Innovative Product Design and Intelligent Manufacturing Systems. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-2696-1_90
Download citation
DOI: https://doi.org/10.1007/978-981-15-2696-1_90
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2695-4
Online ISBN: 978-981-15-2696-1
eBook Packages: EngineeringEngineering (R0)