A Route Planning Approach Using Genetic Algorithm and Costmap for Heterogeneous Multi Unmanned Vehicles

Abstract

In this study, mission assignment and route planning were performed for unmanned aerial, ground, and maritime vehicles (Unmanned Vehicles, UVs). First, a genetic algorithm (GA) was used to determine the mission suitability of the UVs. The various characteristics of the vehicles and environmental factors were taken into account. A costmap-based method was used for safe route planning in obstacle-laden environments. An inflation radius was employed to minimize collisions in obstacle-laden environments. Additionally, safe route planning was performed using fitness values specific to each vehicle type. The proposed method offers a comprehensive framework for heterogeneous vehicle systems, making a significant contribution to the literature. The method's most important feature is its ability to generate optimized solutions tailored to the dynamic needs of different vehicle types. The study presents an innovative framework covering a wide range of vehicles.