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Related Experiment Video

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Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

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3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint.

Mehran Behjati¹, Rosdiadee Nordin¹, Muhammad Aidiel Zulkifley¹

¹Department of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia.

Sensors (Basel, Switzerland)

|November 26, 2022

Summary

This summary is machine-generated.

This study introduces a path planning method for cellular-connected unmanned aerial vehicles (UAVs) to ensure reliable Beyond Visual Line of Sight (BVLOS) flights. The proposed PSO-GA algorithm offers a stable and efficient solution for UAV navigation, validated by real-world 4G network data.

Keywords:

4G GA PSO UAV cellular connected cellular network drone intelligent optimization link reliability path planning trajectory

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Area of Science:

Robotics and Automation
Wireless Communications
Aerospace Engineering

Background:

Unmanned Aerial Vehicles (UAVs) require reliable communication for Beyond Visual Line of Sight (BVLOS) operations.
Cellular networks offer a promising infrastructure for ubiquitous UAV connectivity.
Existing path planning methods often overlook communication reliability and real-world constraints.

Purpose of the Study:

To develop an effective global path planning technique for cellular-connected UAVs.
To enhance the reliability and safety of UAV flights operating BVLOS.
To formulate a path planning problem considering communication link reliability, power consumption, obstacles, and no-fly zones.

Main Methods:

Investigated reliable aerial communication zones using extensive 4G drive tests in a suburban environment.
Formulated the UAV path planning problem with communication link reliability and power consumption constraints.
Employed and combined Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms (PSO-GA) for optimization.

Main Results:

4G base stations provide reliable aerial coverage up to 500m radius and 85m height in suburban areas.
The PSO-GA algorithm demonstrated superior stability, effectiveness, and convergence speed compared to standalone GA and PSO.
PSO-GA achieved approximately twice the execution speed of PSO and GA.

Conclusions:

The proposed path planning method, validated with real-world data, is effective for UAVs in 4G-covered suburban environments.
The PSO-GA algorithm is a robust and efficient solution for UAV global path planning.
Future work can extend the method to 5G networks for enhanced aerial link reliability.