IoT-connected Group Deployment of Unmanned Vehicles with Sensing Units: iUAGV System

Chang, Bao Rong; Tsai, Hsiu-Fen; Lyu, Jyong-Lin; Huang, Chien‐Feng

doi:10.18494/sam.2021.3147

Cited by 3 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This system composes an integrated control interface through UAV, UGV, web server, cloud database, and Android apps [15]. According to the applications of the Internet of Things, unmanned aerial/ground vehicles synchronize the mobile phone, and the app in the mobile phone directly displays the streaming images taken by the camera or the thermal images captured by the infrared thermography on the screen through the wireless network, as shown by arrows 3 and 5 in Figure 1.…”

Section: System Architecturementioning

confidence: 99%

Drone-Aided Path Planning for Unmanned Ground Vehicle Rapid Traversing Obstacle Area

2022

Self Cite

View full text Add to dashboard Cite

Even with visual contact equipment such as cameras, an unmanned ground vehicle (UGV) alone usually takes a lot of time to navigate an unfamiliar area with obstacles. Therefore, this study proposes a fast drone-aided path planning approach to help UGVs traverse an unfamiliar area with obstacles. In this scenario called UAV/UGV mobile collaboration (abbreviated UAGVMC), a UGV initially invokes an unmanned aerial vehicle (UAV) at the scene to take a ground image and send it back to the cloud to proceed with object detection, image recognition, and path planning (abbreviated odirpp). The cloud then sends the UGV a well-planned path map to help traverse an unfamiliar area. This approach uses the one-stage object detection and image recognition algorithm YOLOv4-CSP to quickly and accurately identify obstacles and the New Bidirectional A* (NBA*) algorithm to plan an optimal route avoiding ground objects. Experiments show that the execution time of path planning for each scene is less than 10 s on average. It does not affect the image quality of the path map. It ensures that the user can correctly interpret the path map and remotely drive the UGV rapidly, passing through that unfamiliar area with obstacles. As a result, the selected model can outperform the other alternatives significantly by average performance ratio up to 3.87 times on average.

show abstract

Section: System Architecturementioning

confidence: 99%

Drone-Aided Path Planning for Unmanned Ground Vehicle Rapid Traversing Obstacle Area

2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The recent shift of UAV swarm cooperative networks from theoretical exploration to practical implementation has manifested considerable potential across diverse sectors [1][2][3][4]. Such networks have found applications ranging from military reconnaissance and strike missions to commercial endeavors like environmental monitoring, agricultural spraying, and urban delivery, where their unparalleled efficiency and benefits are evident [5].…”

Section: Introductionmentioning

confidence: 99%

Distributed Computing Optimization in Unmanned Aerial Vehicle Swarm Cooperative Networks

Luo,

Zhang,

Shi

et al. 2024

CIT. J. comput. inf. technol

View full text Add to dashboard Cite

The Unmanned Aerial Vehicle (UAV) swarm cooperative networks across diverse domains present significant challenges in management and control to maximize collaborative efficiency, particularly in applications for large-scale and dynamic environments where real-time coordination is essential. This challenge has led to the development of a UAV swarm cooperative network flow prediction model, facilitating the strategic anticipation of network dynamics. A novel algorithm for distributed, finite-time cooperative control is introduced, grounded in this model. This algorithm optimizes the operational efficiency and control precision of UAV systems, while simultaneously augmenting the collaborative efficiency and stability of the UAV swarms. It marks a significant advancement in distributed computing strategies, offering a viable solution for the real-time coordination challenges in extensive UAV swarm cooperative networks. The proposed approach leverages multi-agent technology, integrating spatial-temporal constraints, location limitations, and path selection in cooperative behavior for dynamic and effective network management. This study not only enhances the understanding of UAV swarm dynamics but also contributes to the practical application of UAV swarms in areas requiring precise coordination and robust network stability.

show abstract

“…The study involved a heterogeneous communication scenario in which consensus control was achieved in the case of changing topology. Furthermore, the authors of [123] introduced a novel communication protocol for the collaboration of air-ground platforms. The cooperative communication scheme allowed inter-communication between UAVs and UGVs through a client-server architecture.…”

mentioning

confidence: 99%

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Wang,

Guo,

Gao

2024

Information

View full text Add to dashboard Cite

Non-terrestrial network (NTN) is a trending topic in the field of communication, as it shows promise for scenarios in which terrestrial infrastructure is unavailable. Unmanned autonomous intelligent systems (UAISs), as a physical form of artificial intelligence (AI), have gained significant attention from academia and industry. These systems have various applications in autonomous driving, logistics, area surveillance, and medical services. With the rapid evolution of information and communication technology (ICT), 5G and beyond-5G communication have enabled numerous intelligent applications through the comprehensive utilization of advanced NTN communication technology and artificial intelligence. To meet the demands of complex tasks in remote or communication-challenged areas, there is an urgent need for reliable, ultra-low latency communication networks to enable unmanned autonomous intelligent systems for applications such as localization, navigation, perception, decision-making, and motion planning. However, in remote areas, reliable communication coverage is not available, which poses a significant challenge for intelligent systems applications. The rapid development of non-terrestrial networks (NTNs) communication has shed new light on intelligent applications that require ubiquitous network connections in space, air, ground, and sea. However, challenges arise when using NTN technology in unmanned autonomous intelligent systems. Our research examines the advancements and obstacles in academic research and industry applications of NTN technology concerning UAIS, which is supported by unmanned aerial vehicles (UAV) and other low-altitude platforms. Nevertheless, edge computing and cloud computing are crucial for unmanned autonomous intelligent systems, which also necessitate distributed computation architectures for computationally intensive tasks and massive data offloading. This paper presents a comprehensive analysis of the opportunities and challenges of unmanned autonomous intelligent systems in UAV NTN, along with NTN-based unmanned autonomous intelligent systems and their applications. A field trial case study is presented to demonstrate the application of NTN in UAIS.

show abstract

IoT-connected Group Deployment of Unmanned Vehicles with Sensing Units: iUAGV System

Cited by 3 publications

References 12 publications

Drone-Aided Path Planning for Unmanned Ground Vehicle Rapid Traversing Obstacle Area

Drone-Aided Path Planning for Unmanned Ground Vehicle Rapid Traversing Obstacle Area

Distributed Computing Optimization in Unmanned Aerial Vehicle Swarm Cooperative Networks

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Contact Info

Product

Resources

About