Dynamic Task Allocation for Collaborative Data Collection: A Vehicle–Drone Approach

Abstract

In recent years, unmanned aerial vehicles (UAVs, also known as drones) have gained widespread application in fields such as data collection and inspection, owing to their lightweight design and high mobility. However, due to limitations in battery life, UAVs are often unable to independently complete large-scale data collection tasks. To address this limitation, vehicle–drone collaborative data collection has emerged as an effective solution. Existing research, however, primarily focuses on collaborative work in static task scenarios, overlooking the complexities of dynamic environments. In dynamic scenarios, tasks may arrive during the execution of both the vehicle and UAV, and each drone has different positions and remaining endurance, creating an asymmetric state. This introduces new challenges for path planning. To tackle this challenge, we propose a 0–1 integer programming model aimed at minimizing the total task completion time. Additionally, we introduce an efficient dynamic solving algorithm, referred to as Greedy and Adaptive Memory Process-based Dynamic Algorithm (GAMPDA). This algorithm first generates an initial global data collection plan based on the initial task nodes and dynamically adjusts the current data collection scheme using a greedy approach as new task nodes arrive during execution. Through comparative experiments, it was demonstrated that GAMPDA outperforms SCAN and LKH in terms of time cost, vehicle travel distance, and drone flight distance and approaches the ideal results. GAMPDA significantly enhances task completion efficiency in dynamic scenarios, providing an effective solution for collaborative data collection tasks in such environments.