Path planning of unmanned aerial vehicles with terrestrial wireless network tracking

Bekhti, Mustapha; Abdennebi, Marwen; Achir, Nadjib; Boussetta, Khaled

doi:10.1109/wd.2016.7461521

Cited by 25 publications

(11 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Online path planning algorithm in a low dangerous environment with static obstacles and dynamic threats was presented in reference 42 . Online path planning approach in 3D was used by researchers in previous works, 33,56 while offline approach in 2D was discussed by authors in references 4,66 . Some authors also explored the offline path planning method in 3D.…”

Section: Aspects Of Path Planning Implementationmentioning

confidence: 99%

See 1 more Smart Citation

Path planning in unmanned aerial vehicles: An optimistic overview

Shahid

Abrar

Ajmal

et al. 2022

Int J Communication

View full text Add to dashboard Cite

Summary With the development in the technology, a rapid increase in the use of unmanned aerial vehicle (UAV) is observed. UAVs are executing tasks that were previously performed by humans or manned aircraft, thus reducing the man workload and saving time. Path planning of UAVs is one of the most researched topics these days. Researchers are investigating more about UAVs and path planning to make it more feasible and economical. The major issue faced while planning a feasible path is to detect and avoid the obstacles encountered during a mission. This paper presents a detailed analysis of path planning in UAVs. Important aspects of path planning, that is, environment, dimensions, obstacles, and type and number of UAVs used in path planning, are also being discussed. Obstacle scenarios which UAV may come across during a mission and constraints which UAV has to follow for successful mission are briefly mentioned. Optimization techniques used to optimize the UAV path are also presented. In addition, future challenges and issues are also discussed.

show abstract

Section: Aspects Of Path Planning Implementationmentioning

confidence: 99%

“…42 Online path planning approach in 3D was used by researchers in previous works, 33,56 while offline approach in 2D was discussed by authors in references. 4,66 Some authors also explored the offline path planning method in 3D. The authors in references 11,67 studied the offline method in 3D environment while keeping the obstacles static.…”

Section: Modementioning

confidence: 99%

Path planning in unmanned aerial vehicles: An optimistic overview

Shahid

Abrar

Ajmal

et al. 2022

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Bekhti and others investigated the path planning of autonomous unmanned aerial vehicles with tracking capabilities provided by terrestrial wireless networks [3]. Shaat and Perez-Neira studied the problem of the cross-layer design of the link scheduling and flow control in a hybrid terrestrial-satellite wireless backhauling network [4].…”

Section: Related Workmentioning

confidence: 99%

Exploiting cognitive wireless nodes for priority‐based data communication in terrestrial sensor networks

Bayrakdar

2019

ETRI Journal

View full text Add to dashboard Cite

A priority-based data communication approach, developed by employing cognitive radio capacity for sensor nodes in a wireless terrestrial sensor network (TSN), has been proposed. Data sensed by a sensor node-an unlicensed user-were prioritized, taking sensed data importance into account. For data of equal priority, a first come first serve algorithm was used. Non-preemptive priority scheduling was adopted, in order not to interrupt any ongoing transmissions. Licensed users used a nonpersistent, slotted, carrier sense multiple access (CSMA) technique, while unlicensed sensor nodes used a nonpersistent CSMA technique for lossless data transmission, in an energy-restricted, TSN environment. Depending on the analytical model, the proposed wireless TSN environment was simulated using Riverbed software, and to analyze sensor network performance, delay, energy, and throughput parameters were examined. Evaluating the proposed approach showed that the average delay for sensed, high priority data was significantly reduced, indicating that maximum throughput had been achieved using wireless sensor nodes with cognitive radio capacity.

show abstract

“…The 3D coordinates of each UAV j ∈ J and each ground user q ∈ Q are, respectively, (x j , y j , h j ) and (x q , y q , 0). All UAVs are assumed to fly at a fixed altitude h j above the ground (as done in [10], [17]- [19]) while the horizonal coordinates (x j , y j ) of each UAV j vary in time. Each UAV j needs to move from an initial location o j to a final destination d j while transmitting online its mission-related data such as sensor recordings, video streams, and location updates.…”

Section: System Modelmentioning

confidence: 99%

Cellular-Connected UAVs over 5G: Deep Reinforcement Learning for Interference Management

Challita,

Saad,

Bettstetter

2018

Preprint

View full text Add to dashboard Cite

In this paper, an interference-aware path planning scheme for a network of cellular-connected unmanned aerial vehicles (UAVs) is proposed. In particular, each UAV aims at achieving a tradeoff between maximizing energy efficiency and minimizing both wireless latency and the interference level caused on the ground network along its path. The problem is cast as a dynamic game among UAVs. To solve this game, a deep reinforcement learning algorithm, based on echo state network (ESN) cells, is proposed. The introduced deep ESN architecture is trained to allow each UAV to map each observation of the network state to an action, with the goal of minimizing a sequence of time-dependent utility functions. Each UAV uses ESN to learn its optimal path, transmission power level, and cell association vector at different locations along its path. The proposed algorithm is shown to reach a subgame perfect Nash equilibrium (SPNE) upon convergence. Moreover, an upper and lower bound for the altitude of the UAVs is derived thus reducing the computational complexity of the proposed algorithm. Simulation results show that the proposed scheme achieves better wireless latency per UAV and rate per ground user (UE) while requiring a number of steps that is comparable to a heuristic baseline that considers moving via the shortest distance towards the corresponding destinations. The results also show that the optimal altitude of the UAVs varies based on the ground network density and the UE data rate requirements and plays a vital role in minimizing the interference level on the ground UEs as well as the wireless transmission delay of the UAV.

show abstract

Path planning of unmanned aerial vehicles with terrestrial wireless network tracking

Cited by 25 publications

References 16 publications

Path planning in unmanned aerial vehicles: An optimistic overview

Path planning in unmanned aerial vehicles: An optimistic overview

Exploiting cognitive wireless nodes for priority‐based data communication in terrestrial sensor networks

Cellular-Connected UAVs over 5G: Deep Reinforcement Learning for Interference Management

Contact Info

Product

Resources

About