TECHNOLOGIES AND METHODS FOR PLANNING THE MOVEMENT OF UAVS ALONG WAYPOINTS
Abstract and keywords
Abstract (English):
Unmanned aerial vehicles (UAVs) occupy a special place in our world. Their ability to navigate along set routes opens up prospects in various fields. The purpose of the study is to review and analyze navigation systems and UAV routing algorithms, methods that allow UAVs to follow the route with high accuracy. GPS and inertial navigation (INS) systems that provide accurate location determination are being investigated. The capabilities of sensor systems — cameras, lidars and ultrasonic sensors — are analyzed to detect obstacles and adjust the trajectory; voxel maps for three-dimensional modeling of the environment and methods of simultaneous localization and mapping (SLAM); algorithm A* (A-star); genetic routing algorithm, potential-based obstacle avoidance algorithms and RRT. Practical significance: the use of these methods and technologies can significantly improve the safety and accuracy of UAV routing, the ability to navigate autonomously in complex and dynamically changing landscapes. In conclusion, the advantages and limitations of navigation approaches and technologies are discussed; the importance of integrating sensor systems and SLAM methods to increase the autonomy and efficiency of UAVs; directions for further research.

Keywords:
UAV routing, GPS, INS, SLAM, ADS-B, voxel maps, sensor systems, genetic algorithm, RRT algorithm
Text
Text (PDF): Read Download
References

1. Elmeseiry N., Alshaer N., Ismail T. A. Detailed Survey and Future Directions of Unmanned Aerial Vehicles (UAVs) with Potential Applications // Aerospace Engineering. 2021. Vol. 8, iss. 12. No. 363. 29 p. DOI:https://doi.org/10.3390/aerospace8120363

2. Groves P. D. Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems. Second Edition. Norwood (MA): Artech House, 2013. 776 p.

3. Roberge V., Tarbouchi M., Labonte G. Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning // IEEE Transactions on Industrial Informatics. 2013. Vol. 9, iss. 1. Pp. 132–141. DOI:https://doi.org/10.1109/TII.2012.2198665

4. Lego T., Fomichev A. V., Lyu Ya. Reshenie zadachi planirovaniya poleta malogabaritnogo bespilotnogo letatel'nogo apparata v usloviyah gorodskoy sredy // Avtomatizaciya. Sovremennye tehnologii. 2015. № 7. S. 19–24.

5. Kuwata Y. Real-Time Trajectory Design for Unmanned Aerial Vehicles Using Receding Horizon Control: A Thesis for the Degree of Master of Science in Aeronautics and Astronautics. Massachusetts Institute of Technology, 2003. 151 p. URL: http://www.researchgate.net/publication/242403825 (assessed: 19.08.2024).

6. Ristić-Durrant D., Franke M., Michels K. A Review of Vision-Based On-Board Obstacle Detection and Distance Estimation in Railways // Sensors. 2021. Vol. 21, iss. 10. No. 3452. 30 p. DOI:https://doi.org/10.3390/s21103452

7. Recent Advances in Unmanned Aerial Vehicles: A Review / F. Ahmed [et al.] // Arabian Journal for Science and Engineering. 2022. Vol. 47, iss. 7. Pp. 7963–7984. DOI:https://doi.org/10.1007/s13369-022-06738-0

8. UAV Path Planning Based on Improved A* and DWA Algorithms / X. Bai [et al.] // International Journal of Aerospace Engineering. 2021. Vol. 2021. No. 4511252. 12 p. DOI:https://doi.org/10.1155/2021/4511252

9. Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age / C. Cadena [et al]. // IEEE Transactions on Robotics. 2016. Vol. 32, iss. 6. Pp. 1309–1332. DOI:https://doi.org/10.1109/TRO.2016.2624754

10. Ubina N. A., Cheng S.-C. A Review of Unmanned System Technologies with Its Application to Aquaculture Farm Monitoring and Management // Drones. 2022, Vol. 6, iss. 1. Art. 12. 41 p. DOI:https://doi.org/10.3390/drones6010012

11. Visual SLAM for Unmanned Aerial Vehicles: Localization and Perception / L. Zhuang [et al.] // Sensors. 2024. Vol. 24, iss. 10. Art. 2980. 24 p. DOI:https://doi.org/10.3390/s24102980

12. Alatorcev D. V., Hamuhin A. V. Analiz effektivnyh metodov ocenki dal'nosti i algoritmov obrabotki videoinformacii na BPLA // Izvestiya Tul'skogo gosudarstvennogo universiteta. Tehnicheskie nauki. 2020. Vyp. 12. C. 255–261.

13. Ali B., Sadekov R. N., Codokova V. V. Algoritmy navigacii bespilotnyh letatel'nyh apparatov s ispol'zovaniem sistem tehnicheskogo zreniya // Giroskopiya i navigaciya. 2022. T. 30, № 4 (119). C. 87–105. DOI:https://doi.org/10.17285/0869-7035.00105

14. Homonenko A. D., Yakovlev E. L. Obosnovanie arhitektury svertochnoy neyronnoy seti dlya avtonomnogo raspoznavaniya ob'ektov na izobrazheniyah bortovoy vychislitel'noy sistemoy // Naukoemkie tehnologii v kosmicheskih issledovaniyah Zemli. 2018. T. 10, № 6. S. 86–93. DOI:https://doi.org/10.24411/2409-5419-2018-10190

15. A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance / P. Fraga-Lamas [et al.] // Remote Sensing. 2019. Vol. 11, iss. 18. Art. 2144. 29 p. DOI:https://doi.org/10.3390/rs11182144

16. Novikov P. A., Homonenko A. D., Yakovlev E. L. Kompleks programm dlya navigacii mobil'nyh ustroystv vnutri pomescheniy s pomosch'yu neyronnyh setey // Informacionno-upravlyayuschie sistemy. 2016. № 1 (80). S. 32– 39. DOI:https://doi.org/10.15217/issn1684-8853.2016.1.32

17. Sposob postroeniya «suboptimal'nyh» marshrutov monitoringa raznotipnyh istochnikov bespilotnym letatel'nym apparatom / A. V. Timoshenko [i dr.] // Trudy MAI. 2020. № 111. 18 c. DOI:https://doi.org/10.34759/trd-2020-111-10

18. Improved Artificial Bee Colony Algorithm-Based Path Planning of Unmanned Autonomous Helicopter Using Multi-Strategy Evolutionary Learning / Z. Han [et al.] // Aerospace Science and Technology. 2022. Vol. 122. Art. 7374. 17 p. DOI:https://doi.org/10.1016/j.ast.2022.107374

19. Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization / M. Li [et al.] // IEEE Transactions on Vehicular Technology. 2020. Vol. 69, iss. 3. Pp. 3424–3438. DOI: 10.1109/ TVT.2020.2968343

20. Energy-Efficient Trajectory Optimization for UAV-Assisted IoT Networks / L. Zhang [et al.] // IEEE Transactions on Mobile Computing. 2021. Vol. 21, iss. 12. Pp. 4323–4337. DOI:https://doi.org/10.1109/TMC.2021.3075083

21. Resource Allocation and Trajectory Optimization for QoE Provisioning in Energy-Efficient UAV-Enabled Wireless Networks / F. Zeng [et al.] // IEEE Transactions on Vehicular Technology. 2020. Vol. 69, iss. 7. Pp. 7634–7647. DOI:https://doi.org/10.1109/TVT.2020.2986776

Login or Create
* Forgot password?