Abstract
To perform complex tasks, drones must have the ability to move autonomously. Ensuring collision avoidance is very important for the safe movement of autonomous drones indoors. FIRAS function-based potential field method is the standard for collision avoidance as implemented in isolated drones. However, its use in an autonomous swarm can be problematic. Its complex interconnected structure causes one of the known issues when there are multiple simultaneously active control objectives. They are intra-swarm collision avoidance and reaching the target relative distance (normally referred to as formation control). This paper shows that with collision avoidance active, the standard potential field method will cause a local minima-like effect in an autonomous swarm. To prevent it, this paper proposes a modified curl-free vector field-based algorithm. This modification enables extended lateral circular motion to prevent swarm members from getting stuck in a local minimum. Stability theory methods are invoked to show that the formation remains stable when running the proposed algorithm. Comparative numerical experiments were run on a drone swarm model in MATLAB/Simulink to illustrate the functioning of this algorithm. To prove the proposed method effective, the paper presents simulation results for standard vs modified potential field.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zakiev, A., Tsoy, T., Magid, E.: Swarm robotics: remarks on terminology and classification. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.) ICR 2018. LNCS (LNAI and LNB), vol. 11097, pp. 291–300. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99582-3_30
Petrenko, V., Tebueva, F., Antonov, V., Ryabtsev, S., Sakolchik, A., Satybaldina, D.: Evaluation of the iterative method of task distribution in a swarm of unmanned aerial vehicles in a clustered field of targets. J. King Saud Univ.-Comput. Inf. Sci. 35, 283–291 (2023). https://doi.org/10.1016/j.jksuci.2023.02.022
Darush, Z., Martynov, M., Fedoseev, A., Shcherbak, A., Tsetserukou, D.: SwarmGear: heterogeneous swarm of drones with reconfigurable leader drone and virtual impedance links for multi-robot inspection (2023). http://arxiv.org/abs/2304.02956
Popov, A.M., Kostrygin, D.G., Shevchik, A.A., Andrievsky, B.: Speed-gradient adaptive control for parametrically uncertain UAVs in formation. Electron. 11, 4187 (2022). https://doi.org/10.3390/ELECTRONICS11244187
Muslimov, T.Z., Munasypov, R.A.: Multi-UAV cooperative target tracking via consensus-based guidance vector fields and fuzzy MRAC. Aircr. Eng. Aerosp. Technol. 93, 1204–1212 (2021). https://doi.org/10.1108/AEAT-02-2021-0058
Izhboldina, V., Lebedev, I.: Group movement of UAVs in environment with dynamic obstacles: a survey. Int. J. Intell. Unmanned Syst. 11, 256–284 (2022). https://doi.org/10.1108/IJIUS-06-2021-0038
Pshikhopov, V., Medvedev, M., Kostjukov, V., Houssein, F., Kadhim, A.: Trajectory planning algorithms in two-dimensional environment with obstacles. Inform. Autom. 21, 459–492 (2022). https://doi.org/10.15622/ia.21.3.1
de Angelis, E.L., Giulietti, F., Rossetti, G.: Multirotor aircraft formation flight control with collision avoidance capability. Aerosp. Sci. Technol. 77, 733–741 (2018). https://doi.org/10.1016/J.AST.2018.04.002
Karkoub, M., Atınç, G., Stipanovic, D., Voulgaris, P., Hwang, A.: Trajectory tracking control of unicycle robots with collision avoidance and connectivity maintenance. J. Intell. Robot. Syst. 96, 331–343 (2019). https://doi.org/10.1007/s10846-019-00987-2
Qiao, Y., et al.: Formation tracking control for multi-agent systems with collision avoidance and connectivity maintenance. In: Drones 2022, vol. 6, p. 419 (2022). https://doi.org/10.3390/DRONES6120419
Wang, N., Dai, J., Ying, J.: UAV formation obstacle avoidance control algorithm based on improved artificial potential field and consensus. Int. J. Aeronaut. Sp. Sci. 22, 1413–1427 (2021). https://doi.org/10.1007/S42405-021-00407-6/
Choi, D., Lee, K., Kim, D.: Enhanced potential field-based collision avoidance for unmanned aerial vehicles in a dynamic environment. In: AIAA Scitech 2020 Forum. American Institute of Aeronautics and Astronautics, Reston, Virginia (2020). https://doi.org/10.2514/6.2020-0487
Choi, D., Kim, D., Lee, K.: Enhanced potential field-based collision avoidance in cluttered three-dimensional urban environments. Appl. Sci. 11, 11003 (2021). https://doi.org/10.3390/APP112211003
Dang, A.D., La, H.M., Nguyen, T., Horn, J.: Formation control for autonomous robots with collision and obstacle avoidance using a rotational and repulsive force–based approach. Int. J. Adv. Robot. Syst. 16, 172988141984789 (2019). https://doi.org/10.1177/1729881419847897
Toksoz, M.A., Oguz, S., Gazi, V.: Decentralized formation control of a swarm of quadrotor helicopters. In: 2019 IEEE 15th International Conference on Control and Automation (ICCA), pp. 1006–1013. IEEE (2019). https://doi.org/10.1109/ICCA.2019.8899628
Acknowledgements
The study was funded by a grant from the Russian Science Foundation (RSF) (project â„– 22-79-00168), https://rscf.ru/en/project/22-79-00168/.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Muslimov, T. (2023). Curl-Free Vector Field for Collision Avoidance in a Swarm of Autonomous Drones. In: Ronzhin, A., Sadigov, A., Meshcheryakov, R. (eds) Interactive Collaborative Robotics. ICR 2023. Lecture Notes in Computer Science(), vol 14214. Springer, Cham. https://doi.org/10.1007/978-3-031-43111-1_33
Download citation
DOI: https://doi.org/10.1007/978-3-031-43111-1_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43110-4
Online ISBN: 978-3-031-43111-1
eBook Packages: Computer ScienceComputer Science (R0)