Joint Position and Travel Path Optimization for Energy Efficient Wireless Data Gathering Using Unmanned Aerial Vehicles

Ghorbel, Mahdi Ben; Rodriguez-Duarte, D. O.; Ghazzai, Hakim; Hossain, Md. Jahangir; Menouar, Hamid

doi:10.1109/tvt.2019.2893374

Cited by 117 publications

(39 citation statements)

References 25 publications

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“…e main constraints considered in the planning process are safety, flight capability, and other constraints: path safety means that the planned path can avoid the threat of obstacles in the flight area and environment, and there will be no collisions between aircraft. Path flightability means that the planned path can meet the kinematic constraints of each UAV, such as the minimum turning radius constraint, path curvature continuity constraint, and maximum climbing angle constraint [14,15]. Other constraints include time coordination constraint and maximum path length constraint.…”

Section: Path Optimization Methods For Multiplementioning

confidence: 99%

Virtual Reality Technology of Multi UAVEarthquake Disaster Path Optimization

Wang

Liu

2021

Mathematical Problems in Engineering

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China is a country with frequent earthquake disasters. After the occurrence of earthquake disasters, the key to disaster monitoring and rescue is to quickly obtain images of postdisaster areas. Unmanned aerial vehicle (UAV) path planning is the core of multi-UAV cooperative control. With the increasing popularity of UAVs, people with more complex living environments contact with UAVs more frequently, which also poses a challenge to the overall control of UAVs, making single UAV and even multi-UAV cooperative path planning become a hot research issue in recent years. The complexity of communication between aircraft in three-dimensional flight space and multidegree of freedom navigation makes multi-UAV cooperation more challenging. According to the research results at home and abroad, this paper takes multitarget tracking algorithm, ant colony algorithm, and hybrid particle swarm optimization algorithm as research methods. Based on virtual reality technology, by comparing the advantages and disadvantages of several algorithms, the research model of path optimization is established, and a multitarget detection method based on virtual reality technology is established. Through the analysis and improvement of multitarget tracking algorithm, ant colony algorithm, and hybrid particle swarm optimization algorithm, the path optimization problem of UAV after an earthquake based on virtual reality technology is studied. The results show that, compared with the previous research models, the overall optimization efficiency of UAV route is improved by 15%, which is more practical.

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Section: Path Optimization Methods For Multiplementioning

confidence: 99%

Virtual Reality Technology of Multi UAVEarthquake Disaster Path Optimization

Wang

Liu

2021

Mathematical Problems in Engineering

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“…In the field of broadened mobile crowd perception (MCS), the fixed-wing UAV-assisted MCS system is taken as the research object, and the corresponding joint path planning and task allocation problems are studied from the perspective of energy efficiency, and the original NP-hard joint optimization problem is transformed for the bilateral two-stage matching problem, this method has achieved good results in energy consumption, overall profit and matching performance [68]. To achieve maximize the power consumption of drones and sensors and terminal compliance to ensure that all data is collected efficiently with minimum energy consumption [69]. When a group of heterogeneous fixed-wing UAVs are traversing multiple targets and performing continuous tasks, to determine the optimal flight trajectory, a coupled distributed planning method combining task assignment and trajectory generation is proposed.…”

Section: C) Optimal Path Planning Technologymentioning

confidence: 99%

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

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The dynamic uncertain environment and complex tasks determine that the unmanned aerial vehicle (UAV) system is bound to develop towards clustering, autonomy，and intelligence. In this paper, we present a comprehensive survey of UAV swarm intelligence from the hierarchical framework perspective. Firstly, we review the basics and advances of UAV swarm intelligent technology. Then we look inside to investigate the research work by classifying UAV swarm intelligence research into five layers, i.e., decision-making layer, path planning layer, control layer, communication layer, and application layer. Furthermore, the relationship between each level is explicitly illustrated, and the research trends of each layer are given. Finally, limitations and possible technology trends of swarm intelligence are also covered to enable further research interests. Through this in-depth literature review, we intend to provide novel insights into the latest technologies in UAV swarm intelligence.

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“…Jointly, an optimization of the UAV communication scheduling and three-dimensional (3D) trajectory is taken into consideration. In [23], multiple optimization problems are sequentially solved to group ground sensors into clusters, determine UAV data collection stops, and provide the path that the UAV should follow to complete its tour in an energy-efficient manner. In [24], a Mixed-Integer Linear Program (MILP) was formulated to minimize the total traveled distance of the UAVs when collecting data from dispersed ground sensors.…”

Section: A Literature Reviewmentioning

confidence: 99%

A UAV-Assisted Data Collection for Wireless Sensor Networks: Autonomous Navigation and Scheduling

et al. 2020

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Nowadays, Wireless Sensor Networks (WSNs) are playing a vital and sustainable role in many verticals touching different aspects of our lives including civil, public, and military applications. WSNs majorly consist of a few to several sensor nodes, that are connected to each other via wireless communication links and require real-time or delayed data transfer. In this paper, we propose an autonomous Unmanned Aerial Vehicle (UAV)-enabled data gathering mechanism for delay-tolerant WSN applications. The objective is to employ a self-trained UAV as a flying mobile unit collecting data from ground sensor nodes spatially distributed in a given geographical area during a predefined period of time. In this approach, two Reinforcement Learning (RL) approaches, specifically Deep Deterministic Gradient Decent (DDPG) and Q-learning (QL) algorithms, are jointly employed to train the UAV to understand the environment and provide effective scheduling to accomplish its data collection mission. The DDPG is used to autonomously decide the best trajectory to adopt in an obstacle-constrained environment, while the QL is developed to determine the order of nodes to visit such that the data collection time is minimized. The schedule is obtained while considering the limited battery capacity of the flying unit, its need to return the charging station, the time windows of data acquisition, and the priority of certain sensor nodes. Customized reward functions are designed for each RL model and, through numerical simulations, we investigate their training performances. We also analyze the behavior of the autonomous UAV for different selected scenarios and corroborate the ability of the proposed approach in performing effective data collection. A comparison with the deterministic optimal solution is provided to validate the performance of the learning-based approach. INDEX TERMS Internet-of-things, data gathering, reinforcement learning, scheduling, unmanned areal vehicles

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Joint Position and Travel Path Optimization for Energy Efficient Wireless Data Gathering Using Unmanned Aerial Vehicles

Cited by 117 publications

References 25 publications

Virtual Reality Technology of Multi UAVEarthquake Disaster Path Optimization

Virtual Reality Technology of Multi UAVEarthquake Disaster Path Optimization

UAV Swarm Intelligence: Recent Advances and Future Trends

A UAV-Assisted Data Collection for Wireless Sensor Networks: Autonomous Navigation and Scheduling

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