Energy-optimal path planning for solar-powered UAVs monitoring stationary target

In this paper, based on the Particle Swarm Optimization (PSO) Algorithms and a novel penalty function, a hybrid method is proposed to plan the optimal trajectories of solar-powered UAVs (SUAVs) for monitoring stationary target. The goal of the route planning is to obtain the maximum net energy when the SUAVs have accomplished a mission under various constraints, e.g., aircraft dynamic constraint and simultaneous arrival at the given destination. First, the target surveillance problem take into account of energy optimization is modeled detailed by formulating the energy harvesting, the energy consumption, the sensor coverage area, the space constraints, etc. Second, based on the mode of stationary target surveillance, the problem of path planning is converted to the nonlinear optimization problem with constraints, and then, by using penalty function method the constrained optimization problem will be transformed to an unconstrained optimization problem. Next, in consideration of the computational complexity of this problem, PSO with penalty function, a novel intelligent algorithm with the advantages of good stability and strong search ability is adopted to solve the optimization problem. Finally, the proposed method is demonstrated and compared with traditional method in the simulated scenario. The simulation results show that it is feasible for this proposed hybrid method to solve the problem of energy optimal path planning.