Multi-UAV path planning for forest fire-fighting in plateau based on adaptive improved dung beetle optimization algorithm

Aiming at the problem of UAV fire-fighting and rescue path planning in the complex environment of plateau forest, a multi-UAV path planning method based on an adaptive improved dung beetle optimization (AIDBO) algorithm was proposed. Based on the digital elevation model (DEM) data, a three-dimensional geospatial model of the plateau forest and mountainous area was established. Considering the influence of complex terrain, environmental wind, altitude and other factors, a multi-UAV path planning model under multiple constraints was established, taking the minimum flight time, risk cost and energy consumption as the objective function. Chaotic mapping and reverse learning optimize the population initialization, the whale optimization algorithm (WOA) introduces the spiral search strategy to enhance the position update mechanism, and adaptive Cauchy mutation improves the population’s capacity to eliminate local optima. An AIDBO algorithm is proposed to solve the problem. The results showed that the average fitness of AIDBO was 10.59% and 9.82% higher than that of the original dung beetle optimization (DBO) algorithm and the improved dung beetle optimization (IDBO) algorithm, respectively. Compared with a 340 m plain environment, the maximum flight time of the UAV in a 2400 m altitude environment is slightly increased by 0.997%, while the energy consumption is increased by 58.96%. At the same time, the wind direction and speed will also have a certain impact on the flight time and energy consumption. In addition to providing decision assistance for UAV firefighting and rescue scheduling in the plateau forests, the adaptive improved dung beetle algorithm presented in this research may greatly optimize the multi-UAV path in the plateau forests.