Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey

• URL: http://arxiv.org/abs/2211.04324v1
• Date: Mon, 7 Nov 2022 15:34:36 GMT
• Title: Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey
• Authors: Harrison Kurunathan, Hailong Huang, Kai Li, Wei Ni, and Ekram Hossain
• Abstract summary: The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications.
• Score: 43.573379573511765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.

Related papers

• Integrating Large Language Models for UAV Control in Simulated Environments: A Modular Interaction Approach [0.3495246564946556]
  This study explores the application of Large Language Models in UAV control. By enabling UAVs to interpret and respond to natural language commands, LLMs simplify the UAV control and usage. The paper discusses several key areas where LLMs can impact UAV technology, including autonomous decision-making, dynamic mission planning, enhanced situational awareness, and improved safety protocols.
  arXiv  Detail & Related papers  (2024-10-23T06:56:53Z)
• Large Language Models for UAVs: Current State and Pathways to the Future [6.85423435360359]
  Unmanned Aerial Vehicles (UAVs) have emerged as a transformative technology across diverse sectors. This work explores the significant potential of integrating UAVs and Large Language Models (LLMs) to propel the development of autonomous systems.
  arXiv  Detail & Related papers  (2024-05-02T21:30:10Z)
• Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach [57.15309977293297]
  The synergy between Unmanned Aerial Vehicles (UAVs) and metaverses is giving rise to an emerging paradigm named UAV metaverses. We propose a tiny machine learning-based Stackelberg game framework based on pruning techniques for efficient UT migration in UAV metaverses.
  arXiv  Detail & Related papers  (2024-01-18T02:14:13Z)
• Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities [59.02391344178202]
  Vision foundation models (VFMs) serve as potent building blocks for a wide range of AI applications. The scarcity of comprehensive training data, the need for multi-sensor integration, and the diverse task-specific architectures pose significant obstacles to the development of VFMs. This paper delves into the critical challenge of forging VFMs tailored specifically for autonomous driving, while also outlining future directions.
  arXiv  Detail & Related papers  (2024-01-16T01:57:24Z)
• Evidential Detection and Tracking Collaboration: New Problem, Benchmark and Algorithm for Robust Anti-UAV System [56.51247807483176]
  Unmanned Aerial Vehicles (UAVs) have been widely used in many areas, including transportation, surveillance, and military. Previous works have simplified such an anti-UAV task as a tracking problem, where prior information of UAVs is always provided. In this paper, we first formulate a new and practical anti-UAV problem featuring the UAVs perception in complex scenes without prior UAVs information.
  arXiv  Detail & Related papers  (2023-06-27T19:30:23Z)
• Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics [31.921859542234998]
  Distributed learning (DL) enables UAV swarms to intelligently provide communication services, multi-directional remote surveillance, and target tracking. We first introduce several popular DL algorithms such as federated learning (FL), multi-agent Reinforcement Learning (MARL), distributed inference, and split learning. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such us reconfigurable intelligent surface (RIS), virtual reality (VR), semantic communications, and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications.
  arXiv  Detail & Related papers  (2023-01-03T01:05:18Z)
• Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies [56.77079930521082]
  We have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage.
  arXiv  Detail & Related papers  (2022-12-20T15:26:39Z)
• Machine Learning Methods for Management UAV Flocks -- a Survey [3.190574537106449]
  UAV technology can be used in a wide range of domains, including communication, agriculture, security, and transportation. It may be useful to group the UAVs into clusters/flocks in certain domains, and various challenges associated with UAV usage can be alleviated by clustering. Several computational challenges arise in UAV flock management, which can be solved by using machine learning (ML) methods.
  arXiv  Detail & Related papers  (2021-08-30T18:03:34Z)
• Serverless Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities [27.366598254560994]
  Unmanned aerial vehicles (UAVs) are envisioned to support extensive applications in next-generation wireless networks in both civil and military fields. We propose a novel architecture called SELF-UN, which enables FL within UAV networks without a central entity.
  arXiv  Detail & Related papers  (2021-04-15T16:11:13Z)
• Artificial Intelligence for UAV-enabled Wireless Networks: A Survey [72.10851256475742]
  Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Artificial intelligence (AI) is growing rapidly nowadays and has been very successful. We provide a comprehensive overview of some potential applications of AI in UAV-based networks.
  arXiv  Detail & Related papers  (2020-09-24T07:11:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.