Late acceptance hill climbing based algorithm for Unmanned Aerial Vehicles (UAV) path planning problem

Abstract

This research innovatively applies the Late Acceptance Hill-Climbing (LAHC) algorithm to unmanned aerial vehicle (UAV) path planning in complex urban environments, marked by irregularly shaped threat areas. Moving beyond conventional models that simplify threat representations, our approach meticulously calculates collision costs, factoring in both the size and proximity of threat areas to waypoints, thereby enhancing path safety and feasibility. The standout feature of the LAHC algorithm is its memory-based strategy, which is simple yet remarkably effective, as evidenced by its superior performance over leading meta-heuristic algorithms across various urban flight scenarios. The study’s findings are significant, revealing that LAHC not only excels in optimizing path costs but also outperforms in terms of the number of iterations to convergence and average execution time. Additionally, a detailed convergence and complexity analysis of LAHC is conducted, providing deeper insights into its operational efficiency. Key contributions of this study include the development of realistic benchmark flight environments, the introduction of a novel method for collision cost calculation in urban settings, and the successful demonstration of LAHC’s rapid optimization capabilities and high-quality solutions.