Improved Green Anaconda Optimization Algorithm-based Coverage Path Planning Mechanism for heterogeneous unmanned aerial vehicles

Abstract

The advancement of artificial intelligence and autonomous control has resulted in the widespread use of unmanned aerial vehicles (UAVs) in a variety of large-scale practical applications like target tracking, disaster surveillance, and traffic monitoring. Heterogeneous UAVs outperform homogeneous UAVs in terms of energy consumption and performance. The use of several unmanned aerial vehicles (UAVs) inside broad cooperative search systems, including numerous separate locations, provides the difficulty of sophisticated path planning. The computational complexity of NP-hard problems makes coverage path planning a difficult challenge to solve. This difficulty stems from the need to establish the most effective paths for unmanned aerial vehicles (UAVs) to thoroughly explore selected areas of interest. In this paper, Improved Green Anaconda Optimization Algorithm-based Coverage Path Planning Mechanism is proposed for handling the problem of coverage path planning in UAVs. It specifically adopted an improved Green Anaconda Optimization System (IGAOS) to determines possible and potential paths for the UAVs to fully cover the complete regions of interest in an efficient manner. Initially, the regions and models of UAVs are established using linear programming for identifying the best-to-point flight path for each UAV. It is proposed for minimizing the tasks’ time consumption in the system of cooperative search through the exploration of optimal solution depending on the inspiration derived from the hunting and mating strategy of green anacondas. Experiments on deviation ratio, task completion time, and execution time with this IGAOS revealed its advantages over prior PPSOESSA, HFACPP, ACSCPP, and GAGPSCPP approaches.