An improved multi-objective optimization problem model for enhancing UAV path planning

Abstract

Unmanned Aerial Vehicles (UAVs) have become crucial in various industries, such as agriculture, construction and mining, infrastructure inspection, environmental monitoring, and emergency response. These diverse applications underscore the importance of UAV drone path planning for enhancing efficiency and safety. This work builds upon the study presented by Wang et al., highlighting limitations in environmental modeling. The failure to accurately replicate the environmental conditions can be attributed to insufficient documentation of the modeling methodology, hindering the repeatability and robustness of the findings. Critiques also target the fitness functions lacking theoretical grounding. The Threat Index assesses flight smoothness but lacks clear operational descriptions, while the Concealment Index evaluates safety but suffers from unclear procedures. There is a need for an improved and accurate model.

Our contribution introduces two new functions for UAV path planning, optimizing the two distinct aspects: the Threat and Concealment of the trajectory. The first proposed function focuses on the distance of the path to the surface, incorporating altitude variations and terrain features to minimize deviations from the surface. The second one addresses angular preferences, minimizing deviations from a straight-line trajectory to reduce the impact of inertia on UAV dynamics. The integration of the new objective functions contributes to a multi-objective optimization framework, balancing considerations of path proximity to the surface and path linearity for enhanced UAV path planning performance. Our framework involved conducting four test scenarios with distinct points of origin and goals, utilizing the SMS-EMOA algorithm to find the best path. Each experiment was characterized by unique initial and terminal coordinates, allowing for a comprehensive evaluation across diverse scenarios. The evolutionary algorithms were configured with specific parameters to balance computational efficiency with optimization robustness. Additionally, 30 independent runs were performed for each scenario, comparing the two sets of objective functions to capture the general behavior of each one. The success of the experiments was measured by the convergence of the algorithms towards Pareto-optimal solutions, demonstrating adaptability and effectiveness across varied spatial scenarios. Wang et al.’s framework contrasts with ours by focusing on comparing the performance of an improved NSGA-II algorithm adapted to the problem context.

Their analysis revealed that their improved NSGA-II algorithm outperformed NSGA-II regarding route length and threat reduction, with modest improvements in concealment. Our framework offers a comprehensive and systematic approach to evaluation. Through multiple experiments across diverse scenarios and specific parameters, it provides a thorough understanding of algorithm performance under various conditions.