A Review of Task Allocation Methods for UAVs

Abstract

Unmanned aerial vehicles, can offer solutions to a lot of problems, making it crucial to research more and improve the task allocation methods used. In this survey, the main approaches used for task allocation in applications involving UAVs are presented as well as the most common applications of UAVs that require the application of task allocation methods. They are followed by the categories of the task allocation algorithms used, with the main focus being on more recent works. Our analysis of these methods focuses primarily on their complexity, optimality, and scalability. Additionally, the communication schemes commonly utilized are presented, as well as the impact of uncertainty on task allocation of UAVs. Finally, these methods are compared based on the aforementioned criteria, suggesting the most promising approaches.

Learning-in-Games Approach for the Mission Planning of Autonomous Multi-Drone Spatio-Temporal Sensing

“…In regard to multi-drone task allocation, references [24] and [25] provides a detailed survey of the most recent decentralized algorithms. Apart from the distinction in centralized and decentralized approaches, these algorithms may be divided in [25]:…”

“…This combination is usually adopted to meet the requirements of a specific application. In particular, game-theoretic approaches for multi-drone task allocation generally [25]: (i) demonstrate greater efficiency with respect to other decentralized approaches, producing suboptimal solutions that are closer to the optimal one; (ii) have comparable or lower complexity with respect to CBBA approaches and are suitable for large-scale systems; (iii) have limited communication burden, especially in the case of competitive algorithms (i.e., using non-cooperative game settings) [29].…”