A 3D Flight Route Planning Algorithm for UAV Based on the Idea of Resilience

Xu, Zhining; Yang, Lin; Yang, Feng; Zhang, Long; Ma, Xiaoshan; Liu, Yang

doi:10.1145/3497623.3497668

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…-Transmission parameters configuration: In FANETs, at the beginning of each transmission frame, the transmission parameters, such as noise power, the service rate each UAV requests, and fading coefficient of each A2A channel and A2G channel between every two UAVs, and between every base station (BS) and each UAV, respectively (Xu et al, 2021) need to be configured optimally to utilize wireless resources efficiently and to achieve reliable communication (Bithas et al, 2019).…”

Section: Analysis Of the Specific Questionsmentioning

confidence: 99%

A systematic literature review of flying ad hoc networks: State‐of‐the‐art, challenges, and perspectives

Pasandideh

Costa

Kunst

et al. 2023

Journal of Field Robotics

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Unmanned aerial vehicles (UAVs), also known as drones, communicate, collaborate, and form flying ad hoc networks (FANETs) to perform many different missions, ranging from delivery tasks to agriculture applications. Recently, FANETs have been integrated with different technologies, such as artificial intelligence (AI), virtual reality, and Internet of Things. Such new avenues for the use of UAVs directly impact the research on FANETs and cause some major challenges, such as security and physical layer issues, resource management, and UAV positioning issues that need to be addressed. Several researchers have been working for the last few years to propose AI and machine learning (ML)‐based solutions for different use cases in UAV‐based networks. They present the limitations of the existing research work and highlight some possible future works on FANETs. However, exhibiting the trends in the UAV research papers in a quantitative manner is still required to motivate researchers to rethink the research on FANETs. Therefore, this study covers more than 170 scientific publications extracted from five trusted academic databases published from 2013 to 2021 to provide a thorough overview of the main research and development statistics in the area of FANETs, the open challenges existing in this area and the ML‐based solutions to solve these challenges. In addition, the investigation of emerging technologies integrated with FANETs, as well as the simulation tools employed for evaluating FANETs' performance are discussed. Moreover, the future research directions in the area of FANETs are considered within a prospective vision discussion.

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Section: Analysis Of the Specific Questionsmentioning

confidence: 99%

A systematic literature review of flying ad hoc networks: State‐of‐the‐art, challenges, and perspectives

Pasandideh

Costa

Kunst

et al. 2023

Journal of Field Robotics

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“…To solve the problems of autonomous anti-disturbance, obstacle avoidance and planning of UAVs, a variety of UAV-assisted algorithms for specific needs have been developed. Traditional UAV autonomous route planning algorithms mainly include the A* algorithm [ 1 , 2 , 3 ], ant colony algorithm [ 4 , 5 , 6 , 7 ], simulated annealing algorithm [ 8 , 9 , 10 , 11 ], Particle Swarm algorithm [ 12 , 13 , 14 ], artificial potential fields algorithm [ 15 , 16 , 17 , 18 , 19 ], etc. The A* algorithm, represented by a small amount of calculations and fast speed, has difficulties ensuring the safe operation of the UAV in an environment involving disturbance.…”

Section: Introductionmentioning

confidence: 99%

An Anti-Disturbance Resilience Enhanced Algorithm for UAV 3D Route Planning

Zhang

et al. 2022

Sensors

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Considering that the actual operating environment of UAV is complex and easily disturbed by the space environment of urban buildings, the RoutE Planning Algorithm of Resilience Enhancement (REPARE) for UAV 3D route planning based on the A* algorithm and artificial potential fields algorithm is carried out in a targeted manner. First of all, in order to ensure the safety of the UAV design, we focus on the capabilities of the UAV body and build a risk identification, assessment, and modeling method such that the mission control parameters of the UAV can be determined. Then, the three-dimensional route planning algorithm based on the artificial potential fields algorithm is used to ensure the safe operation of the UAV online and in real time. At the same time, by adjusting the discriminant coefficient of potential risks in real time to deal with time-varying random disturbance encountered by the UAV, the resilience of the UAV 3D flight route planning can be improved. Finally, the effectiveness of the algorithm is verified by the simulation. The simulation results show that the REPARE algorithm can effectively solve the traditional route planning algorithm’s insufficiency in anti-disturbance. It is safer than a traditional A* route planning algorithm, and its running time is shorter than that of the traditional artificial potential field route planning algorithm. It solves the problems of local optimization, enhances the UAV’s ability to tolerate general uncertain disturbances, and eventually improves resilience of the system.

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A 3D Flight Route Planning Algorithm for UAV Based on the Idea of Resilience

Cited by 2 publications

References 10 publications

A systematic literature review of flying ad hoc networks: State‐of‐the‐art, challenges, and perspectives

A systematic literature review of flying ad hoc networks: State‐of‐the‐art, challenges, and perspectives

An Anti-Disturbance Resilience Enhanced Algorithm for UAV 3D Route Planning

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