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

• Published in: Sustainable computing informatics and systems Vol. 42; p. 100961
• Main Authors: Karthik, K., Balasubramanian, C
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.04.2024
• Subjects: Coverage path planning; Improved Green Anaconda Optimization Algorithm (IGAOA); Linear programming; NP-hard problem; Unmanned aerial vehicles (UAV); Unmanned aerial vehicles (UAV); Linear programming; NP-hard problem; Coverage path planning; Improved Green Anaconda Optimization Algorithm (IGAOA)
• DOI: 10.1016/j.suscom.2024.100961
• ISSN: 2210-5379

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. Unmanned aerial Vehicles (UAV) with the reliable characteristics of flexibility and effectiveness is extensively utilized in the large-scale practical applications that includes target tracking, disaster surveillance and traffic monitoring due to the rapid advent of artificial intelligence and automatic control. Coverage path planning problem pertains to NP-hard computation complexity problem, and it is identified to be difficult to handle as it necessitates the determination of optimal paths for UAVs to explore the complete regions 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 related to UAVs. It specifically adopted an improved Green Anaconda Optimization System (IGAOS) to determines possible and potential paths for the UAVs such that the complete regions of interest are fully covered in an efficient manner.•Utilized Improved Green Anaconda Optimization Algorithm to allocate regions for suitable UAVs using regions visiting order.•Used linear programming model with constraints for find best paths of flights to each UAVs in a multiple region optimally.•Execution time of the proposed IGAOS scheme for 20 no. of randomly generated regions is 134.8 sec which is 3.2 times better.•Task completion time of the proposed IGAOS scheme with varying no. of regions is improved by 3.8 times to the existing ones.