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Heterogeneous Formation Sliding Mode Control of the Flying Robot and Obstacles Avoidance

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Abstract

The purpose of this article is to control the formation of heterogeneous flying robots and to cross obstacles. The robots in question are a quadrotor and two unmanned helicopters. Independent attitude and position of robots and formation flight are controlled by the sliding mode method. Improved Artificial Potential Fields has been used to avoid collision with dynamic and static obstacles. The results of the design of the attitude and position controller showed that the attitude and position of the robots were well stabilized and converged in less than 3 s, which is a favorable time. The results of the formation flight simulation were presented in the form of 4 missions. In the first mission, the quadrotor is considered the leader, and the helicopters are the followers. The flight formation is triangular and the flight path is spiral. The results showed that the followers follow the leader. In the second mission, the robot crosses dynamic and static obstacles and the leader tracks the fixed target. In the third mission, the number of followers increases to 5 and the flight formation is hexagonal. The obstacles in this mission are dynamic and the target is moving. The results showed that in these two missions, the leader tracks the target well and the robots maintain triangular and hexagonal flight formations after crossing the obstacles. The results of simulations of group crossing of obstacles showed that the simulation error is less than 4% according to the expected position of the robots, and show the efficiency of the applied methods.

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Abbreviations

SMC:

Sliding Mode Control

STSMC:

Super-Twisting Sliding Mode Control

APF:

Artificial Potential Field

UAV:

Unmanned Aerial Vehicles

UA-GV:

Unmanned Air-Ground Vehicles

References

  1. Nguyen, N.P., Park, D., Ngoc, D.N., Xuan-Mung, N., Huynh, T.T., Nguyen, T.N., Hong, S.K.: Quadrotor formation control via terminal sliding mode approach: theory and experiment results. Drones 6(7), 172 (2022). https://doi.org/10.3390/drones6070172

    Article  Google Scholar 

  2. Mechali, O., Xu, L., Xie, X., Iqbal, J.: Theory and practice for autonomous formation flight of quadrotors via distributed robust sliding mode control protocol with fixed-time stability guarantee. Control Eng. Pract. 123, 105150 (2022). https://doi.org/10.1016/j.conengprac.2022.105150

    Article  Google Scholar 

  3. Mechali, O., Xu, L., Xie, X.: Nonlinear homogeneous sliding mode approach for fixed-time robust formation tracking control of networked quadrotors. Aerosp. Sci. Technol. 126, 107639 (2022). https://doi.org/10.1016/j.ast.2022.107639

    Article  Google Scholar 

  4. González-Sierra, J., Dzul, A., Martínez, E.: Formation control of distance and orientation based-model of an omnidirectional robot and a quadrotor UAV. Robot. Auton. Syst. 147, 103921 (2022). https://doi.org/10.1016/j.robot.2021.103921

    Article  Google Scholar 

  5. Cheng, W., Zhang, K., Jiang, B., Ding, S.X.: Fixed-time fault-tolerant formation control for heterogeneous multi-agent systems with parameter uncertainties and disturbances. IEEE Trans. Circuits Syst. I Regul. Pap. 68(5), 2121–2133 (2021). https://doi.org/10.1109/TCSI.2021.3061386

    Article  MathSciNet  Google Scholar 

  6. ong, G., Ma, J., Jiang, Y., Mao, Z.: Distributed adaptive fault-tolerant formation control for heterogeneous multiagent systems under switching directed topologies. J. Franklin Inst. 359(8), 3366–3388 (2022). https://doi.org/10.1016/j.jfranklin.2022.03.048

    Article  MathSciNet  Google Scholar 

  7. Zhao, W., Liu, H., Wan, Y., Lin, Z.: Data-driven formation control for multiple heterogeneous vehicles in air–ground coordination. IEEE Trans. Control Netw. Syst. 9(4), 1851–1862 (2022). https://doi.org/10.1109/TCNS.2022.3181254

    Article  MathSciNet  Google Scholar 

  8. Yu, D., Zhou, P., Jing, Y.: Optimal obstacle avoidance consensus formation control method for fixed-wing UAV with variable topology. Aerosp. Syst. 5(1), 75–84 (2022). https://doi.org/10.1007/s42401-021-00119-5

    Article  Google Scholar 

  9. Cong, Y., Du, H., Jin, Q., Zhu, W., Lin, X.: Formation control for multiquadrotor aircraft: connectivity preserving and collision avoidance. Int. J. Robust Nonlinear Control 30(6), 2352–2366 (2020). https://doi.org/10.1002/rnc.4886

    Article  MathSciNet  Google Scholar 

  10. Vargas, S., Becerra, H.M., Hayet, J.B.: MPC-based distributed formation control of multiple quadcopters with obstacle avoidance and connectivity maintenance. Control. Eng. Pract. 121, 105054 (2022). https://doi.org/10.1016/j.conengprac.2021.105054

    Article  Google Scholar 

  11. Yan, S., Pan, F.: Research on route planning of auv based on genetic algorithms. In: 2019 IEEE International Conference on Unmanned Systems and Artificial Intelligence (ICUSAI), pp. 184–187, IEEE, (2019). https://doi.org/10.1109/ICUSAI47366.2019.9124785

  12. MahmoudZadeh, S., Powers, D.M., Yazdani, A.M., Sammut, K., Atyabi, A.: Efficient AUV path planning in time-variant underwater environment using differential evolution algorithm. J. Mar. Sci. Appl. 17, 585–591 (2018). https://doi.org/10.1007/s11804-018-0034-4

    Article  Google Scholar 

  13. Che, G., Liu, L., Yu, Z.: An improved ant colony optimization algorithm based on particle swarm optimization algorithm for path planning of autonomous underwater vehicle. J. Ambient Intell. Humaniz Comput. 11, 3349–3354 (2020). https://doi.org/10.1007/s12652-019-01531-8

    Article  Google Scholar 

  14. Lim, H.S., Fan, S., Chin, C.K., Chai, S., Bose, N.: Particle swarm optimization algorithms with selective differential evolution for AUV path planning. Int. J. Robot. Autom. 9(2), 94–112 (2020). https://doi.org/10.11591/ijra.v9i2

    Article  Google Scholar 

  15. Li, X., Wang, W., Song, J., Liu, D.: Path planning for autonomous underwater vehicle in presence of moving obstacle based on three inputs fuzzy logic. In: 2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), pp. 265–268, IEEE, (2019). https://doi.org/10.1109/ACIRS.2019.8936029

  16. Sun, B., Zhu, D., Tian, C., Luo, C.: Complete coverage autonomous underwater vehicles path planning based on glasius bio-inspired neural network algorithm for discrete and centralized programming. IEEE Trans. Cogn. Dev. Syst. 11(1), 73–84 (2018). https://doi.org/10.1109/TCDS.2018.2810235

    Article  Google Scholar 

  17. Taheri, E., Ferdowsi, M.H., Danesh, M.: Closed-loop randomized kinodynamic path planning for an autonomous underwater vehicle. Appl. Ocean Res. 83, 48–64 (2019). https://doi.org/10.1016/j.apor.2018.12.008

    Article  Google Scholar 

  18. Zhao, Y., Hao, L.Y., Wu, Z.J.: Obstacle avoidance control of unmanned aerial vehicle with motor loss-of-effectiveness fault based on improved artificial potential field. Sustainability 15(3), 2368 (2023). https://doi.org/10.3390/su15032368

    Article  Google Scholar 

  19. Havenstrøm, S.T., Rasheed, A., San, O.: Deep reinforcement learning controller for 3D path following and collision avoidance by autonomous underwater vehicles. Front. Rob. AI. 211 (2021). https://doi.org/10.3389/frobt.2020.566037

  20. Pan, Z., Zhang, C., Xia, Y., Xiong, H., Shao, X.: An improved artificial potential field method for path planning and formation control of the multi-UAV systems. IEEE Trans. Circuits Syst. II Express Briefs 69(3), 1129–1133 (2021). https://doi.org/10.1109/TCSII.2021.3112787

    Article  Google Scholar 

  21. Huang, Y., Tang, J., Lao, S.: UAV group formation collision avoidance method based on second-order consensus algorithm and improved artificial potential field. Symmetry 11(9), 1162 (2019). https://doi.org/10.3390/sym11091162

    Article  Google Scholar 

  22. Chang, K., Ma, D., Han, X., Liu, N., Zhao, P.: Lyapunov vector-based formation tracking control for unmanned aerial vehicles with obstacle/collision avoidance. Trans. Inst. Meas. Control. 42(5), 942–950 (2020). https://doi.org/10.1177/0142331219879338

    Article  Google Scholar 

  23. Zhao, T., Zhang, J., Rong, K., Zhang, W.: Collision Avoidance Algorithm for UAV Formation Reconfiguration Under UV Non-uniform Virtual Potential Field. 电子与信息学报, 44, 1–9, (2022). https://doi.org/10.11999/JEIT220442

  24. Ning, W., Jiyang, D., Jin, Y.: UAV formation recovery and consistency Simulation based on improved potential field. J. Syst. Simulation, 34(5), 978, (2022). https://doi.org/10.16182/j.issn1004731x.joss.20-0980

  25. Prouty, R.: Helicopter performance, stability, and control, pp. 143– 146, 476–477. Krieger Publishing Company, Malabar (1990)

  26. Pounds, P., Mahony, R., Corke, P.: Modelling and control of a quad-rotor robot. In: Proceedings of the 2006 Australasian Conference on Robotics and Automation, Australian Robotics and Automation Association (ARAA), pp. 1–10, (2006)

  27. Newman, S.: The foundations of helicopter flight, pp. 107–116. Halsted Press, New York, NY (1994)

    Google Scholar 

  28. Hoffmann, G., Waslander, S.L., Huang, H., Tomlin, C.J.: Autonomous Quadrotor helicopter testbed design, control, and experiments. AIAA Journal of Guidance, Control, and Dynamics. (2007)

  29. Joelianto, E., Maryami Sumarjono, E., Budiyono, A., Retnaning Penggalih, D.: Model predictive control for autonomous unmanned helicopters. Aircr. Eng. Aerosp. Technol. 83(6), 375–387 (2011). https://doi.org/10.1108/00022661111173252

    Article  Google Scholar 

  30. Bangura, M., Mahony, R.: Nonlinear dynamic modeling for high performance control of a quadrotor. (2012). https://doi.org/10.1109/ICInfA.2015.7279823

  31. Mechali, O., Iqbal, J., Mechali, A., Xie, X., Xu, L.: Finite-time attitude control of uncertain quadrotor aircraft via continuous terminal sliding-mode-based active anti-disturbance approach. In: 2021 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 1170–1175, IEEE, (2021). https://doi.org/10.1109/ICMA52036.2021.9512751

  32. Ghadiri, H., Emami, M., Khodadadi, H.: Adaptive super-twisting non-singular terminal sliding mode control for tracking of quadrotor with bounded disturbances. Aerosp. Sci. Technol. 112, 106616 (2021). https://doi.org/10.1016/j.ast.2021.106616

    Article  Google Scholar 

  33. Kotov, K.Y., Mal’tsev, A.S., Nesterov, A.A., Sobolev, M.A., Yan, A.P.: Decentralized control of quadrotors in a leader–follower formation. Optoelectron. Instrum. Data Process. 53, 21–25 (2017). https://doi.org/10.3103/S8756699017010046

    Article  Google Scholar 

  34. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Robot. Res. 5(1), 90–98 (1986). https://doi.org/10.1177/027836498600500106

    Article  Google Scholar 

  35. Zhou, P., Lai, S., Cui, J., Chen, B.M.: Formation control of unmanned rotorcraft systems with state constraints and inter-agent collision avoidance. Auton. Intell. Syst. 3(1), 4 (2023). https://doi.org/10.1007/s43684-023-00049-3

    Article  Google Scholar 

  36. Liu, H., Tu, H., Huang, S., Zheng, X.: Adaptive predefined-time sliding mode control for QUADROTOR formation with obstacle and inter-quadrotor avoidance. Sensors 23(5), 2392 (2023). https://doi.org/10.3390/s23052392

    Article  Google Scholar 

  37. Li, B., Gong, W., Yang, Y., Xiao, B.: Distributed fixed-time leader-following formation control for multi-quadrotors with prescribed performance and collision avoidance. IEEE Trans. Aerosp. Electron. Syst. (2023). https://doi.org/10.1109/TAES.2023.3289480

    Article  Google Scholar 

  38. Ma, Z., Wang, Q., Chen, H.: A joint guidance and control framework for autonomous obstacle avoidance in quadrotor formations under model uncertainty. Aerosp. Sci. Technol. 138, 108335 (2023). https://doi.org/10.1016/j.ast.2023.108335

    Article  Google Scholar 

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Authors and Affiliations

  1. Shahid Beheshti University, Tehran, 1983969411, Iran

    Fatemeh Ghaderi & Alireza Toloei

  2. Universiy of Qom, Qom, 3716146611, Iran

    Reza Ghasemi

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  1. Fatemeh Ghaderi
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Correspondence to Alireza Toloei.

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Ghaderi, F., Toloei, A. & Ghasemi, R. Heterogeneous Formation Sliding Mode Control of the Flying Robot and Obstacles Avoidance. Int. J. ITS Res. (2024). https://doi.org/10.1007/s13177-024-00396-2

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