Self-adaptive asymmetrical artificial potential field approach dedicated to the problem of position tracking by nonholonomic uavs in windy enivroments

• Autorzy: Kownacki Cezary

Identyfikatory

DOI

10.2478/ama-2021-0006

• Języki publikacji: EN

Abstrakty

EN

Artificial potential fields (APFs) are a popular method of planning and controlling the path of robot movement, including unmanned aerial vehicles (UAVs). However, in the case of nonholonomic robots such as fixed-wing UAVs, the distribution of velocity vectors should be adapted to their limited manoeuvrability to ensure stable and precise position tracking. The previously proposed local asymmetrical potential field resolves this issue, but it is not effective in the case of windy environments, where the UAV is unable to maintain the desired position and drifts due to the wind drift effect. This is reflected in the growth of position error, which, similar to the steady-state error in the best case, is constant. To compensate for it, the asymmetrical potential field approach is modified by extending definitions of potential function gradient and velocity vector field (VVF) with elements based on the integral of position tracking error. In the case of wind drift, the value of this integral increases over time, and lengths and orientations of velocity vectors will also be changed. The work proves that redefining gradient and velocity vector as a function of position tracking error integrals allows for minimisation of the position tracking error caused by wind drift.

Słowa kluczowe

EN

artificial potential field; asymmetrical potential field; position tracking; UAV; self-adaptive potential field

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Acta Mechanica et Automatica
• Rocznik: 2021
• Tom: Vol. 15, no. 1
• Strony: 37--46
• Opis fizyczny: Bibliogr. 28 poz., rys., wykr.

Twórcy

autor

Kownacki Cezary

• *Department of Robotics and Mechatronics, Faculty of Mechanical Engineering, Bialystok University of Technology, ul. Wiejska 45C, 15-351 Białystok, Poland

Bibliografia

• 1. Ambroziak L., Gosiewski Z. (2015), Two Stage Switching Control for Autonomous Formation Flight of Unmanned Aerial Vehicles, Aerospace Science and Technology, Vol. 46, 2015, pp. 221-226.
• 2. Ambroziak L., Kondratiuk M., Ciezkowski M., Kownacki C. (2018), Hardware in the Loop Tests of the Potential Field-Based Algorithm for Formation Flight Control of Unmanned Aerial Vehicles, Mechatronic Systems and Materials 2018, Zakopane, AIP Conference Proceedings 2029, 020002-1–020002-10.
• 3. Barnes L., Fields M. and Valavanis K. (2007), Unmanned Ground Vehicle Swarm Formation Control Using Potential Fields, in Mediterranean Conference on Control and Automation, Athens.
• 4. Bennet D. J., McInnes C. R. (2008) Space Craft Formation Flying Using Bifurcating Potential Fields, in International Astronautical Congress.
• 5. Bennet D. J., McInnes C. R. (2011), Autonomous ThreeDimensional Formation Flight for a Swarm of Unmanned Aerial Vehicles, Journal of Gudiance, Control, and Dynamics, vol. 34, no. 6, pp. 1899-1908.
• 6. Budiyanto A., Cahyadi A., Adji T. B., Wahyunggoro O. (2015), UAV Obstacle Avoidance Using Potential Field Under Dynamic Environment, in 2015 International Conference on Control, Electronics, Renewable Energy and Communications, Bandung.
• 7. Cetin O., Yilmaz G. (2016), Real-time Autonomous UAV Formation Flight with Collision and Obstacle Avoidance in Unknown Environment, Journal of Intelligent & Robotic Systems, vol. 84, no. 1, pp. 415-433.
• 8. Chen Y., Luo G., Mei Y., Yu J. and Su X. (2016), UAV Path Planning Using Artificial Potential Field Method Updated by Optimal Control Theory, International Journal of System Science, vol. 47, no. 6, pp. 1407-1420.
• 9. Chen Y., Yu J., Su X., Luo G. (2015), Path Planning for Multi-UAV Formation, Journal of Intelligent & Robotic Systems, vol. 77, no. 1, pp. 229-246.
• 10. Frew E. W., Lawrence D. A., Dixon C., Elston J., Pisano W. J. (2007), Lyapunov Guidance Vector Fields for Unmnned Aircraft Applications, in IEEE American Control Conference.
• 11. Gosiewski Z., Ambroziak L. (2012), Formation Flight Control Scheme for Unmanned Aerial Vehicles, Lecture Notes in Control and Information Science, vol. 422, pp. 331-340, 2012,
• 12. Hatton R.L., Choset H. (2011). Geometric Motion Planning: the Local Connection, Stokes’ Theorem, and the Importance of Coordinate Choice. The International Journal of Robotics Research, 30(8), pp.988-1014,
• 13. Khuswendi T., Hindersah H., Adiprawita W. (2011), UAV Path Planning Using Potential Field and Modified Receding Horizon A* 3D Algorithm, in Proceedings of the 2011 International Conference on Electrical Engineering and Informatics.
• 14. Kokume N., Uchiyama K. (2010), Guidance Law Based on Bifurcating Velocity Field for Formation Flight, in AIAA Guidance, Navigation, and Control Conference.
• 15. Kowalczyk W., Kozłowski K. (2004), Artificial Potential Based Control for a Large-Scale Formation of Mobile Robots., in Proceedings of the Fourth International Workshop on Robot Motion and Control.
• 16. Kownacki C. (2016), Multi-UAV Flight Using Virtual Structure Combined with Behavioral Approach, Acta Mechanica et Automatica, Vol. 10, No 2. 92-99.
• 17. Kownacki C., Ambroziak L. (2017), Local and Asymmetrical Potential Field Approach to Leader Tracking Problem in Rigid Formations of Fixed-Wing UAVs, Aerospace Science and Technology, Vol. 68, September 2017, pp. 465-474.
• 18. Kownacki C., Ambroziak L. (2019), Adaptation Mechanism of Asymmetrical Potential Field Improving Precision of Position Tracking in the Case of Non-Holonomic UAVs, Robotica, doi: https://doi.org/10.1017/S0263574719000286, published online: 10 April 2019, pp. 1-12,
• 19. Kownacki C., Ołdziej O. (2016), Fixed-wing UAVs Flock Control through Cohesion and Repulsion Behaviours Combined with a Leadership, International Journal of Advanced Robotic Systems, vol. 13, p. DOI: 10.5772/62249.
• 20. Li K., Han X., Qi G. (2009), Formation and Obstacle-Avoidance control for Mobile Swarm Robots Based on Artificial Potential Field, in Conference on Robotics and Biomimetics.
• 21. Mukherjee R., Anderson D.P. (1993), Nonholonomic Motion Planning Using Stokes’ Theorem. In: IEEE International Conference on Robotics and Automation, pp. 802–809.
• 22. Nagao Y., Uchiyama K. (2014), Formation Flight of Fixed-Wing UAVs Using Artificial Potential Field, in 29th Congress of the International Council of the Aerospace Sciences, St. Petersburg.
• 23. Nelson D. R., Barber D. B., McLain T. W., Beard R. W. (2007), Vector Field Path Following for Miniature Air Vehicles, IEEE Transactions on Robotics, vol. 23, no. 3, pp. 519-529.
• 24. Nieuwenhuisen M., Schadler M., Behnke S. (2013), Predictive Potential Field-Based Collision Avoidance for Multicopters, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vols. XL-1/W2.
• 25. Suzuki M., Uchiyama K. (2010), Autonomous Formation Flight Using Bifurcating Potential Fields, in 27th International Congress of the Aeronautical Sciences, Nice.
• 26. Suzuki M., Uchiyama K. (2011), Three-Dimensional Formation Flying Using Bifurcating Potential Fields, in AIAA Guidance, Navigation, and Control Conference, Chicago.
• 27. Tobias P., Krogstad T. R., Gravdahl J. T. (2008), UAV Formation Flight Using 3D Potential Field, in 16th Mediterranean Conference on Control and Automation.
• 28. Virágh C., Vásárhelyi G., Tarcai N., Szörényi T., Somorjai G., Nepusz T., Vicsek T. (2014), Flocking Algorithm for Autonomous Flying Robots, Bioinspiration & Biomimetics, vol. 9, no. 2, p. 025012.