Title: Path planning of hovercraft using an adaptive ant colony with an artificial potential field algorithm

Authors: Zain Anwar Ali; Zhangang Han

Addresses: School of Systems Science, Beijing Normal University, Zhuhai 519085, China ' School of Systems Science, Beijing Normal University, Zhuhai 519085, China

Abstract: This study designs a novel strategy by combining the adaptive ant colony optimisation (ACO) method with the artificial potential field (APF) algorithm. The classic ACO algorithm has multiple limitations like falling into local optimum, slow convergence rate, etc. This hybrid strategy aims to counter the aforementioned problems. This study discusses the previous and current works in the concerned research area to better understand the solutions available and then try to improve them further. Then, this paper presents the mathematical model of the hovercraft. Afterward, this study designs the novel hybrid method by using the adaptive ACO in conjunction with the APF method. We use two different scenarios in simulation to test the validity of the designed strategy. First, we test the hybrid method in an environment with predetermined obstacles. Secondly, we use a dynamic mission area with shifting obstacles to further prove the efficiency of the designed method. The simulation results prove that the designed strategy is more effective and robust than traditional ACO. It converges quicker and finds the optimal path.

Keywords: adaptive ant colony; artificial potential field; APF; path planning; hovercraft.

DOI: 10.1504/IJMIC.2021.123820

International Journal of Modelling, Identification and Control, 2021 Vol.39 No.4, pp.350 - 356

Received: 12 Jan 2021
Accepted: 03 Jun 2021

Published online: 04 Jul 2022 *

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