Hybrid Optimization Based Multi-Objective Path Planning Framework for Unmanned Aerial Vehicles

Abstract

Unmanned aerial vehicles (UAVs) have been touted as a cost-effective way to follow sensitive resources across wide areas. The extensive wireless sensor networks that UAVs travel through have allowed them to acquire exceptional amounts of data. The path planning of UAV operations in low-altitude urban environments is the main emphasis of this research. Regarding two objectives, namely journey distance and safety level, a Multi-Objective Path Planning (MOPP) framework is introduced. A unique hybrid optimization approach is used to optimally find the best flight path between adjacent acquisition stations based on these MOPPs. The conceptual fusion of traditional DHOA with WOA results in the suggested hybrid model known as Deer Hunter Updated Whale Optimization (DHUWO). Ultimately, the projected collision-free optimal path framework referred to Multi-Objective Path Planning with DHUWO (MOPP-DHUWO) model is compared over the existing models in terms of safety level and distance as well.