An Efficient UAS Path Planning Strategy Based on Improved Imperialist Competitive Algorithm

Abstract

Daily growth of unmanned aerial systems (UAS) technology in areas related to geospatial sciences and location-based services makes it easy to employ them in digital photogrammetry, enironment monitoring, infrastructures, sensitive constructions and medical support and aid delivery tasks. In this area, fast and efficient development in UAS path planning can enhance the productivity, efficiency and quality of these missions. In this research, an efficient UAS path planning strategy is proposed based on the imperialist competitive optimization algorithm, which can improve the productivity and autonomy of different missions in new challenges such as medical support and aid delivery scenarios. For this purpose, first, with respect to the objectives and restrictions of the scenario, a single objective constrained optimization model is designed. Then, to intensify the exploration and exploitation capabilities of imperialist competitive algorithm, the assimilation step is integrated with gravity physics. In simulation phase, the results obtained by proposed method has been compared to the results of the other variants of this method with respect to the quality, standard deviation, running time, success rate and quality of the computed paths. The assessment in multiple simulations verifies the efficiency and robustness of the proposed strategy and quality of the obtained paths in studied scenario.