TY - JOUR
T1 - Pigeon-inspired optimisation-based cooperative target searching for multi-UAV in uncertain environment
AU - Luo, Delin
AU - Li, Sijie
AU - Shao, Jiang
AU - Xu, Yang
AU - Liu, Yong
N1 - Publisher Copyright:
Copyright © 2022 Inderscience Enterprises Ltd.
PY - 2022
Y1 - 2022
N2 - In this paper, the multi-UAV cooperative target searching problem is investigated and a close loop path planning method is developed for UAVs in uncertain environment. The proposed method includes two consecutive parts, the multi-UAV cooperative target search algorithm developed based on cooperative pigeon-inspired optimisation (CPIO) and the base returning algorithm for each UAV based on artificial potential field (APF) method. Firstly, a concerned regional environment and the initial search probability map models are established. Then, by applying the rolling prediction strategy, the cooperative target search paths for multiple UAVs are generated by utilising the proposed CPIO. With this method, UAVs can reinforce target search in the key areas in a cooperative way and avoid flying into the no-fly zones. In the meanwhile, the Bayesian theorem is used to constantly update the search probability map in each search step. Finally, at the end of the target search phase, an optimised safe path is generated for each UAV returning back to its original by using the APF method. Simulations are performed and the results demonstrate that the proposed approach is effective for multiple UAVs carrying out cooperative target search task in a complex environment.
AB - In this paper, the multi-UAV cooperative target searching problem is investigated and a close loop path planning method is developed for UAVs in uncertain environment. The proposed method includes two consecutive parts, the multi-UAV cooperative target search algorithm developed based on cooperative pigeon-inspired optimisation (CPIO) and the base returning algorithm for each UAV based on artificial potential field (APF) method. Firstly, a concerned regional environment and the initial search probability map models are established. Then, by applying the rolling prediction strategy, the cooperative target search paths for multiple UAVs are generated by utilising the proposed CPIO. With this method, UAVs can reinforce target search in the key areas in a cooperative way and avoid flying into the no-fly zones. In the meanwhile, the Bayesian theorem is used to constantly update the search probability map in each search step. Finally, at the end of the target search phase, an optimised safe path is generated for each UAV returning back to its original by using the APF method. Simulations are performed and the results demonstrate that the proposed approach is effective for multiple UAVs carrying out cooperative target search task in a complex environment.
KW - APF
KW - artificial potential field
KW - cooperative search
KW - multi-UAV
KW - pigeon-inspired algorithm
UR - http://www.scopus.com/inward/record.url?scp=85131423432&partnerID=8YFLogxK
U2 - 10.1504/IJBIC.2022.123107
DO - 10.1504/IJBIC.2022.123107
M3 - 文章
AN - SCOPUS:85131423432
SN - 1758-0366
VL - 19
SP - 158
EP - 168
JO - International Journal of Bio-Inspired Computation
JF - International Journal of Bio-Inspired Computation
IS - 3
ER -