Cooperative Task Assignment for Aerial-Ground Detection Systems via a Novel Hybrid Genetic Method

This study focuses on cooperative task assignment for aerial-ground detection systems utilizing a hybrid genetic algorithm (HGA) with dual-layer chromosomes. It addresses nonroad network constraints, task unreachability, and practical factors such as power, capacity, and waiting time. A mixed integer linear programming model is established for aerial-ground cooperative task assignment. A HGA with dual-layer chromosome encoding is designed to solve the aerial-ground cooperative task assignment problem quickly. In addition, the proposed method demonstrates shorter task durations and higher efficiency than the state-of-the-art algorithms. Finally, synthetic and real-world examples are provided to illustrate the effectiveness and advantages of the method gathered in the article.

I. Introduction

Over the past decades, the development of robotic technology has led to the flourishing of multirobot systems to perform complex tasks. Moreover, aerial-ground robot systems have continued to capture the attention of researchers with enhanced adaptability, efficiency, and flexibility. In addition, aerial-ground robot systems have numerous applications in environmental monitoring [1], [2], last-mile logistics [3], data collection [4], signal relay [5], large-scale patrol [6], as well as search and rescue [7].