Scalable and Cooperative Deep Reinforcement Learning Approaches for Multi-UAV Systems: A Systematic Review
In recent years, the use of multiple unmanned aerial vehicles (UAVs) in various applications has progressively increased thanks to advancements in multi-agent system technology, which enables the accomplishment of complex tasks that require cooperative and coordinated abilities. In this article, mul...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/7/4/236 |
| _version_ | 1797605707518836736 |
|---|---|
| author | Francesco Frattolillo Damiano Brunori Luca Iocchi |
| author_facet | Francesco Frattolillo Damiano Brunori Luca Iocchi |
| author_sort | Francesco Frattolillo |
| collection | DOAJ |
| description | In recent years, the use of multiple unmanned aerial vehicles (UAVs) in various applications has progressively increased thanks to advancements in multi-agent system technology, which enables the accomplishment of complex tasks that require cooperative and coordinated abilities. In this article, multi-UAV applications are grouped into five classes based on their primary task: coverage, adversarial search and game, computational offloading, communication, and target-driven navigation. By employing a systematic review approach, we select the most significant works that use deep reinforcement learning (DRL) techniques for cooperative and scalable multi-UAV systems and discuss their features using extensive and constructive critical reasoning. Finally, we present the most likely and promising research directions by highlighting the limitations of the currently held assumptions and the constraints when dealing with collaborative DRL-based multi-UAV systems. The suggested areas of research can enhance the transfer of knowledge from simulations to real-world environments and can increase the responsiveness and safety of UAV systems. |
| first_indexed | 2024-03-11T05:04:54Z |
| format | Article |
| id | doaj.art-3cb58abb16f24499a1c15b0146b718ea |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-11T05:04:54Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-3cb58abb16f24499a1c15b0146b718ea2023-11-17T18:57:54ZengMDPI AGDrones2504-446X2023-03-017423610.3390/drones7040236Scalable and Cooperative Deep Reinforcement Learning Approaches for Multi-UAV Systems: A Systematic ReviewFrancesco Frattolillo0Damiano Brunori1Luca Iocchi2Department of Computer, Control and Management Engineering (DIAG), Sapienza University of Rome, 00185 Rome, ItalyDepartment of Computer, Control and Management Engineering (DIAG), Sapienza University of Rome, 00185 Rome, ItalyDepartment of Computer, Control and Management Engineering (DIAG), Sapienza University of Rome, 00185 Rome, ItalyIn recent years, the use of multiple unmanned aerial vehicles (UAVs) in various applications has progressively increased thanks to advancements in multi-agent system technology, which enables the accomplishment of complex tasks that require cooperative and coordinated abilities. In this article, multi-UAV applications are grouped into five classes based on their primary task: coverage, adversarial search and game, computational offloading, communication, and target-driven navigation. By employing a systematic review approach, we select the most significant works that use deep reinforcement learning (DRL) techniques for cooperative and scalable multi-UAV systems and discuss their features using extensive and constructive critical reasoning. Finally, we present the most likely and promising research directions by highlighting the limitations of the currently held assumptions and the constraints when dealing with collaborative DRL-based multi-UAV systems. The suggested areas of research can enhance the transfer of knowledge from simulations to real-world environments and can increase the responsiveness and safety of UAV systems.https://www.mdpi.com/2504-446X/7/4/236unmanned aerial vehiclesmulti-UAVdeep reinforcement learningmulti-agent cooperation |
| spellingShingle | Francesco Frattolillo Damiano Brunori Luca Iocchi Scalable and Cooperative Deep Reinforcement Learning Approaches for Multi-UAV Systems: A Systematic Review Drones unmanned aerial vehicles multi-UAV deep reinforcement learning multi-agent cooperation |
| title | Scalable and Cooperative Deep Reinforcement Learning Approaches for Multi-UAV Systems: A Systematic Review |
| title_full | Scalable and Cooperative Deep Reinforcement Learning Approaches for Multi-UAV Systems: A Systematic Review |
| title_fullStr | Scalable and Cooperative Deep Reinforcement Learning Approaches for Multi-UAV Systems: A Systematic Review |
| title_full_unstemmed | Scalable and Cooperative Deep Reinforcement Learning Approaches for Multi-UAV Systems: A Systematic Review |
| title_short | Scalable and Cooperative Deep Reinforcement Learning Approaches for Multi-UAV Systems: A Systematic Review |
| title_sort | scalable and cooperative deep reinforcement learning approaches for multi uav systems a systematic review |
| topic | unmanned aerial vehicles multi-UAV deep reinforcement learning multi-agent cooperation |
| url | https://www.mdpi.com/2504-446X/7/4/236 |
| work_keys_str_mv | AT francescofrattolillo scalableandcooperativedeepreinforcementlearningapproachesformultiuavsystemsasystematicreview AT damianobrunori scalableandcooperativedeepreinforcementlearningapproachesformultiuavsystemsasystematicreview AT lucaiocchi scalableandcooperativedeepreinforcementlearningapproachesformultiuavsystemsasystematicreview |