An Air-to-Ground Relay Communication Planning Method for UAVs Swarm Applications

Yin, Dong; Yang, Xuan; Yu, Huangchao; Chen, Siyuan; Wang, Chang

doi:10.1109/tiv.2023.3237329

Cited by 33 publications

(11 citation statements)

References 40 publications

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“…The decisions made by the UAV to select actions that represent the targeted hotspot depend on the current word (i.e., the current location of the UAV), which is determined by the probability entries in (21). Thus, discrete actions are sampled from:…”

Section: Action Selectionmentioning

confidence: 99%

“…In this case, the UAV must correct the action selection process by updating its strategy of forming the word. This can be done by updating the active inference table defined in (21) as follows:…”

Section: Abnormality Measures and Action Updatementioning

confidence: 99%

“…Another study proposed a method for planning the trajectory of a UAV to provide emergency data uploading for large-scale dynamic networks [ 20 ]. Multi-hop relay UAV trajectory planning is also crucial in UAV swarm networks [ 21 ]. Joint optimization of the UAV’s trajectory and user association was suggested in [ 22 ] to maximize total throughput and energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Goal-Directed Trajectory Planning Using Active Inference in UAV-Assisted Wireless Networks

Krayani

Khan

Marchese

et al. 2023

Sensors

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Deploying unmanned aerial vehicles (UAVs) as aerial base stations is an exceptional approach to reinforce terrestrial infrastructure owing to their remarkable flexibility and superior agility. However, it is essential to design their flight trajectory effectively to make the most of UAV-assisted wireless communications. This paper presents a novel method for improving wireless connectivity between UAVs and terrestrial users through effective path planning. This is achieved by developing a goal-directed trajectory planning method using active inference. First, we create a global dictionary using traveling salesman problem with profits (TSPWP) instances executed on various training examples. This dictionary represents the world model and contains letters representing available hotspots, tokens representing local paths, and words depicting complete trajectories and hotspot order. By using this world model, the UAV can understand the TSPWP’s decision-making grammar and how to use the available letters to form tokens and words at various levels of abstraction and time scales. With this knowledge, the UAV can assess encountered situations and deduce optimal routes based on the belief encoded in the world model. Our proposed method outperforms traditional Q-learning by providing fast, stable, and reliable solutions with good generalization ability.

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Section: Action Selectionmentioning

confidence: 99%

Section: Abnormality Measures and Action Updatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Goal-Directed Trajectory Planning Using Active Inference in UAV-Assisted Wireless Networks

Krayani

Khan

Marchese

et al. 2023

Sensors

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“…How to design the relay planning framework for AVs' application is a hot topic today [35]. According to UAVs' tasks schedules and task regions in the swarm, we propose a framework for relay UAVs task planning as shown in Figure 2.…”

Section: Relay Communicationsmentioning

confidence: 99%

Communication and Network for Autonomous Vehicles

Yin¹

2023

Autonomous Vehicles - Applications and Perspectives

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The communication and network are the critical issues for autonomous vehicles(AVs). Three main aspects will be illustraed in this chapter including Wireless Communication, Self-Organizing Network and Data Transmission. As a typical representative of autonomous systems, unmanned aerial vehicles (UAVs) are the most widely used and numerous in military and civil fields, most of this chapter focuses on UAVs as technical research objects. Firstly, basic characteristics of wireless communication such as signal propagation and attenuation in different applications and other mobile communication issues for AVs, especially some new technologies which are adaptable, will be presented. Secondly, the features of AVs network architecutre and how to design it will be discussed, while the issues of self-organizing nework for AVs will be investigated. At last, typical data transmission methods such as DDS for AVs will be presented. Also the critical factors should be considerd in designing the data transmission will be illustrated.

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“…The use of air and ground unmanned vehicles has become an important goal for real-world tasks of search-and-rescue scenarios [1][2][3]. Specifically, Unmanned Aerial Vehicles (UAVs) could significantly assist Unmanned Ground Vehicles (UGVs) by providing localization data and serving as communication relays [4]. However, a UAV has limited payload capacity and endurance.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Deep Reinforcement Learning of UAV-UGV Collaborative Landing Policy Using Automatic Curriculum

Wang,

Wei

et al. 2023

Drones

Self Cite

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Collaborative autonomous landing of a quadrotor Unmanned Aerial Vehicle (UAV) on a moving Unmanned Ground Vehicle (UGV) presents challenges due to the need for accurate real-time tracking of the UGV and the adjustment for the landing policy. To address this challenge, we propose a progressive learning framework for generating an optimal landing policy based on vision without the need of communication between the UAV and the UGV. First, we propose the Landing Vision System (LVS) to offer rapid localization and pose estimation of the UGV. Then, we design an Automatic Curriculum Learning (ACL) approach to learn the landing tasks under different conditions of UGV motions and wind interference. Specifically, we introduce a neural network-based difficulty discriminator to schedule the landing tasks according to their levels of difficulty. Our method achieves a higher landing success rate and accuracy compared with the state-of-the-art TD3 reinforcement learning algorithm.

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An Air-to-Ground Relay Communication Planning Method for UAVs Swarm Applications

Cited by 33 publications

References 40 publications

A Goal-Directed Trajectory Planning Using Active Inference in UAV-Assisted Wireless Networks

A Goal-Directed Trajectory Planning Using Active Inference in UAV-Assisted Wireless Networks

Communication and Network for Autonomous Vehicles

Vision-Based Deep Reinforcement Learning of UAV-UGV Collaborative Landing Policy Using Automatic Curriculum

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