TY - GEN
T1 - Optimum Navigation of Four-Wheeled Ground Robot in Stationary and Non-stationary Environments Using Wind-Driven Optimization Algorithm
AU - Bej, Nilotpala
AU - Pandey, Anish
AU - Kashyap, Abhishek K.
AU - Parhi, Dayal R.
N1 - Publisher Copyright:
© 2020, Springer Nature Singapore Pte Ltd.
PY - 2020
Y1 - 2020
N2 - 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).
AB - 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).
UR - https://www.scopus.com/pages/publications/85081886152
UR - https://www.scopus.com/pages/publications/85081886152#tab=citedBy
U2 - 10.1007/978-981-15-2696-1_90
DO - 10.1007/978-981-15-2696-1_90
M3 - Conference contribution
AN - SCOPUS:85081886152
SN - 9789811526954
T3 - Lecture Notes in Mechanical Engineering
SP - 931
EP - 941
BT - Innovative Product Design and Intelligent Manufacturing Systems - Select Proceedings of ICIPDIMS 2019
A2 - Deepak, BBVL.
A2 - Parhi, DRK.
A2 - Jena, Pankaj C.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st International Conference on Innovative Product Design and Intelligent Manufacturing System, ICIPDIMS 2019
Y2 - 17 May 2019 through 18 May 2019
ER -