TY - JOUR
T1 - A novel IPSO technique for path navigation and obstacle avoidance
AU - Mukherjee, Subhradip
AU - Kumar, Rajagopal
AU - Bhattacharjee, Rituraj
N1 - Publisher Copyright:
Copyright © 2021 Inderscience Enterprises Ltd.
PY - 2021
Y1 - 2021
N2 - Path navigation using meta-heuristic optimisation is a popular research topic in the field of autonomous vehicle path planning. In this paper, an improved particle swarm optimisation (IPSO) technique has been presented for unmanned vehicle path navigation which is the enhanced version of existing PSO technique. IPSO is used to solve the convergence speed problem which is a general practice in optimisation algorithms. The modified fitness function of the proposed algorithm is designed in such a manner that causes to converge the algorithm fast. IPSO technique is dependent upon the working of modified fitness function for the path optimisation in an unknown environment. After several iterations, the best cost is computed which is utilised to find the optimised path in mobile robot path navigation. The simulation results clearly indicate that IPSO is competent enough when compared with the existing optimisation methods (genetic algorithm, invasive weed optimisation (IWO)).
AB - Path navigation using meta-heuristic optimisation is a popular research topic in the field of autonomous vehicle path planning. In this paper, an improved particle swarm optimisation (IPSO) technique has been presented for unmanned vehicle path navigation which is the enhanced version of existing PSO technique. IPSO is used to solve the convergence speed problem which is a general practice in optimisation algorithms. The modified fitness function of the proposed algorithm is designed in such a manner that causes to converge the algorithm fast. IPSO technique is dependent upon the working of modified fitness function for the path optimisation in an unknown environment. After several iterations, the best cost is computed which is utilised to find the optimised path in mobile robot path navigation. The simulation results clearly indicate that IPSO is competent enough when compared with the existing optimisation methods (genetic algorithm, invasive weed optimisation (IWO)).
UR - https://www.scopus.com/pages/publications/85127223986
UR - https://www.scopus.com/pages/publications/85127223986#tab=citedBy
U2 - 10.1504/IJSSE.2021.121467
DO - 10.1504/IJSSE.2021.121467
M3 - Article
AN - SCOPUS:85127223986
SN - 1748-0671
VL - 11
SP - 430
EP - 442
JO - International Journal of System of Systems Engineering
JF - International Journal of System of Systems Engineering
IS - 3-4
ER -