Path planning of solar-powered unmanned aerial vehicles at low altitude

In this paper, the path planning strategy for solar powered UAVs at low altitude is examined including the weather factor in energy harvesting. The designed path will maximize the difference between the collected energy and the consumed energy, named net gain of energy, when flying from the specified initial point to the final point. A weather map providing information such as regional precipitation is utilized to predict the solar spectral density for the concerned areas. We propose a graph based approach which divides the concerned areas into small grids to evaluate the solar spectral density corresponding to the local weather and then build the energy intensity distribution map as a function of coordinates. The Bellman-Ford algorithm is utilized to find the optimal path which yields maximum net gain of energy at terminal point. Simulation results for level flight are presented.