Abstract
An improved path planning algorithm for the unmanned aerial vehicles (UAVs) is developed, which is based on the combination of grey wolf optimizer-whale optimization algorithm (GWO-WOA) and fuzzy neural network dynamic window approach (FNN-DWA). It aims to realize dynamic path planning in a spherical obstacle environment. Firstly, an improved GWO and WOA based global planning algorithm is developed, in which an adaptive position updating equation is designed to improve GWO’s convergence speed. Meanwhile, inspired by the spiral bubble nets strategy in WOA and levy flight strategy in cuckoo search algorithm (CS), a random and the best individual’s information based random wandering strategy is developed to improve GWO’s global exploration ability. Then, an improved FNN-DWA based local planning algorithm is proposed, and the path points of GWO-WOA are set as goals for local planning. Based the proposed fuzzy logic based DWA (FDWA), FNN is utilized to train the input membership functions’ parameters and the connection weights. It aims to improve the algorithm’s adaptability to complex dynamic environments. Finally, simulations results are obtained to verify the effective performance of the designed scheme to realize dynamic path planning for the UAV.
This work was supported in part by National Natural Science Foundation of China (62073212), Natural Science Foundation of Shanghai (23ZR1426600), Innovation Fund of Chinese Universities Industry-University-Research (2021ZYB05004).
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Li, B., Wang, S., Luo, W., **ong, H., Temuer, C. (2023). Improved GWO-WOA and Fuzzy NN DWA Based Path Planning Algorithm for the UAV in Dynamic Environment. In: Jia, Y., Zhang, W., Fu, Y., Wang, J. (eds) Proceedings of 2023 Chinese Intelligent Systems Conference. CISC 2023. Lecture Notes in Electrical Engineering, vol 1090. Springer, Singapore. https://doi.org/10.1007/978-981-99-6882-4_8
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