Hybrid golden Jackal and moth flame optimization algorithm based coverage path planning in heterogeneous UAV networks

Coverage Path Planning is an important strategy used mainly in junction with the unmanned aerial vehicles (UAVs) such that it can cover the regions of the target with minimized energy consumptions. This coverage path planning specifically with heterogeneous UAVs despite varying capabilities concentrate on the process of flight paths optimization when is employed to cover a target region. In this paper, a coverage path planning strategy using hybrid Golden Jackal and moth flame optimization algorithm (HGJMFOA) is proposed for estimating superior optimal paths that supports the UAVs towards the process of comprehensively covering the generated regions with efficacy. At the initial phase, the regions are generated randomly and the models of UAVs which is formulated using a linear programming model is included for identifying the best point-to-point flight path for each individual UAVs. Then it adopted the merits of HGJMFOA for exploring and exploiting the feasible paths from the source to the destination from which optimal shortest path can be determined with reduced path flight time. It handled the challenges involved during the process of achieving coverage path planning by establishing cooperation between diversified UAVs such that optimal coverage, covering sensor range and superior flight performance is achieved during the application. The simulation experiments of the proposed HGJMFOA approach confirmed minimized task completion time by 21.34%, deviation ratio by 24.56%. and execution time by 34.19%, with randomly generated regions organized for performance evaluation.