Formation Control and Obstacle Avoidance of a Multi-Quadrotor System Based on Model Predictive Control and Improved Artificial Potential Field

Document Type : Original Article

Authors

1 Department of Aerospace Engineering, Shahid Beheshti University, Tehran, Iran

2 Department of Engineering, University of Qom, Qom, Iran

Abstract

The purpose of this article is to control the formation and pass static and dynamic obstacles for the quadrotor group, maintain the continuity and flight formation after crossing the obstacles, and track the moving target. Model Predictive Control (MPC) method has been used to control the status and position of quadrotors and formation control. Flight formation is based on the leader-follower method, in which the followers maintain a certain angle and distance from the leader using the formation controller. The improved Artificial Potential Field (APF) method has been used to pass obstacles, the main advantage of which compared to the traditional APF is to increase the range of the repulsive force of the obstacles, which solves the problem of getting stuck in the local minimum and not passing through the environments full of obstacles. The results of the design of the attitude and position controller showed that the quadrotors were stabilized and converged in less than 3 seconds. Formation control simulations in the spiral path showed that the followers, follow the leader. The results of the quadrotors passing through the obstacles were presented in four missions. In the first mission, 4 quadrotors crossed static obstacles. In the second mission, 4 quadrotors crossed dynamic obstacles. In these two missions, the quadrotors maintained a square flight formation after crossing the obstacles. In the third mission, the number of quadrotors increased to 6. The leader tracked the moving target and the quadrotors crossing the static obstacles.  In the last mission, the quadrotors passed through the dynamic obstacles and the leader tracked the static target. In these missions, the quadrotors maintain the hexagonal formation after crossing the obstacles. The results simulations showed that the quadrotors crossed the fixed and moving obstacles and after crossing, they preserved the flight formation.

Keywords

Main Subjects

References

  1. Procter S, Secco EL. Design of a biomimetic BLDC driven robotic arm for teleoperation & biomedical applications. J Hum Earth Future ISSN. 2022:2785-997. 10. 28991/HEF-2021-02-04-03
  2. Arofiati F, Sekhar R, Rijalusalam DU. PID-based with Odometry for Trajectory Tracking Control on Four-wheel Omnidirectional Covid-19 Aromatherapy Robot. 2021. 10.28991/esj-2021-SPER-13
  3. Doakhan M, Kabganian M, Azimi A. Cooperative payload transportation with real-time formation control of multi-quadrotors in the presence of uncertainty. Journal of the Franklin Institute. 2023;360(2):1284-307. 10.1016/j.jfranklin.2022.11.002
  4. Andaluz GM, Leica P, Herrera M, Morales L, Camacho O. Hybrid Controller based on Null Space and Consensus Algorithms for Mobile Robot Formation. Emerging Science Journal. 2022;6(3):429-47. 10.28991/ESJ-2022-06-03-01
  5. Chevet T, Vlad C, Maniu CS, Zhang Y. Decentralized MPC for UAVs formation deployment and reconfiguration with multiple outgoing agents. Journal of Intelligent & Robotic Systems. 2020;97:155-70. 10.1007/s10846-019-01025-x
  6. Wang X, Xi L, Chen Y, Lai S, Lin F, Chen BM. Decentralized MPC-based trajectory generation for multiple quadrotors in cluttered environments. Guidance, Navigation and Control. 2021;1(02):2150007. 10.1142/S2737480721500072
  7. Wang B, Han Y, Wang S, Tian D, Cai M, Liu M, et al. A Review of Intelligent Connected Vehicle Cooperative Driving Development. Mathematics. 2022;10(19):3635. 10.3390/math10193635
  8. Zou Y, Meng Z. Coordinated trajectory tracking of multiple vertical take-off and landing UAVs. Automatica. 2019;99:33-40. 10.1016/j.automatica.2018.10.011
  9. Dong X, Zhou Y, Ren Z, Zhong Y. Time-varying formation tracking for second-order multi-agent systems subjected to switching topologies with application to quadrotor formation flying. IEEE Transactions on Industrial Electronics. 2016;64(6):5014-24. 10.1109/TIE.2016.2593656
  10. Du H, Zhu W, Wen G, Duan Z, Lü J. Distributed formation control of multiple quadrotor aircraft based on nonsmooth consensus algorithms. IEEE transactions on cybernetics. 2017;49(1):342-53. 10.1109/TCYB.2017.2777463
  11. Jasim W, Gu D. Robust team formation control for quadrotors. IEEE Transactions on Control Systems Technology. 2017;26(4):1516-23. 10.1109/TCST.2017.2705072
  12. Li M, Zhang H, editors. AUV 3D path planning based on A* algorithm. 2020 Chinese Automation Congress (CAC); 2020: IEEE. 10.1109/CAC51589.2020.9327873
  13. Yan S, Pan F, editors. Research on route planning of AUV based on genetic algorithms. 2019 IEEE International Conference on Unmanned Systems and Artificial Intelligence (ICUSAI); 2019: IEEE. 10.1109/ICUSAI47366.2019.9124785
  14. MahmoudZadeh S, Powers DM, Yazdani AM, Sammut K, Atyabi A. Efficient AUV path planning in time-variant underwater environment using differential evolution algorithm. Journal of Marine Science and Application. 2018;17:585-91. 10.1007/s11804-018-0034-4
  15. 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. Journal of Ambient Intelligence and Humanized Computing. 2020;11:3349-54. 10.1007/s12652-019-01531-8
  16. Lim HS, Fan S, Chin C, Chai S, Bose N. Particle swarm optimization algorithms with selective differential evolution for AUV path planning. 2020. 10.11591/ijra.v9i2
  17. Li X, Wang W, Song J, Liu D, editors. Path planning for autonomous underwater vehicle in presence of moving obstacle based on three inputs fuzzy logic. 2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS); 2019: IEEE. 10.1109/ACIRS.2019.8936029
  18. 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 transactions on cognitive and developmental systems. 2018;11(1):73-84. 10.1109/TCDS.2018.2810235
  19. Taheri E, Ferdowsi MH, Danesh M. Closed-loop randomized kinodynamic path planning for an autonomous underwater vehicle. Applied Ocean Research. 2019;83:48-64. 10.1016/j.apor.2018.12.008
  20. 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. 2023;15(3):2368. 10.3390/su15032368
  21. Shang W, Jing G, Zhang D, Chen T, Liang Q. Adaptive fixed time nonsingular terminal sliding-mode control for quadrotor formation with obstacle and inter-quadrotor avoidance. IEEE Access. 2021;9:60640-57. 10.1109/ACCESS.2021.3074316
  22. Pan Z, Li D, Yang K, Deng H. Multi-robot obstacle avoidance based on the improved artificial potential field and PID adaptive tracking control algorithm. Robotica. 2019;37(11):1883-903. 10.1017/S026357471900033X
  23. Wang X, Zhang J, editors. Path Planning of Load Transportation by Multi-Quadrotor Formation Based on RRT-APF Method. 2022 5th International Conference on Robotics, Control and Automation Engineering (RCAE); 2022: IEEE. : 10.1109/RCAE56054.2022.9995950
  24. Qiao Y, Huang X, Yang B, Geng F, Wang B, Hao M, et al. Formation Tracking Control for Multi-Agent Systems with Collision Avoidance and Connectivity Maintenance. Drones. 2022;6(12):419. 10.3390/drones6120419
  25. Aljassani AM, Ghani SN, Al-Hajjar AM. Enhanced multi-agent systems formation and obstacle avoidance (EMAFOA) control algorithm. Results in Engineering. 2023;18:101151. 10.1016/j.rineng.2023.101151
  26. Huang Y, Liu W, Li B, Yang Y, Xiao B. Finite-time formation tracking control with collision avoidance for quadrotor UAVs. Journal of the Franklin Institute. 2020;357(7):4034-58. 10.1016/j.jfranklin.2020.01.014
  27. Zhang J, Xie F, Yin D, Qi Y, Luo D, editors. Formation Control and Obstacle Avoidance for UAV Group. International Conference on Autonomous Unmanned Systems; 2021: Springer. 10.1007/978-981-16-9492-9_77
  28. Ge SS, Cui YJ. Dynamic motion planning for mobile robots using potential field method. Autonomous robots. 2002;13:207-22. 10.1023/A:1020564024509
  29. Li Q, Wang L, Chen B, Zhou Z, editors. An improved artificial potential field method for solving local minimum problem. 2011 2nd International Conference on Intelligent Control and Information Processing; 2011: IEEE. 10.1109/ICICIP.2011.6008278
  30. Abdellatif RA, El-Badawy AA, editors. Artificial potential field for dynamic obstacle avoidance with mpc-based trajectory tracking for multiple quadrotors. 2020 2nd novel intelligent and leading emerging sciences conference (NILES); 2020: IEEE. 10.1109/NILES50944.2020.9257973
  31. Du Y, Zhang X, Nie Z. A real-time collision avoidance strategy in dynamic airspace based on dynamic artificial potential field algorithm. IEEE Access. 2019;7:169469-79. 10.1109/ACCESS.2019.2953946
  32. Ghaderi F, Toloei A, Ghasemi R. Formation control of unmanned helicopters using the sliding mode controller method. Technology in Aerospace Engineering. 2023:1-11. 10.22034/jtae.2023.359555.1263
  33. Kuriki Y, Namerikawa T. Formation control with collision avoidance for a multi-UAV system using decentralized MPC and consensus-based control. SICE Journal of Control, Measurement, and System Integration. 2015;8(4):285-94. 10.9746/jcmsi.8.285
  34. Joelianto E, Maryami Sumarjono E, Budiyono A, Retnaning Penggalih D. Model predictive control for autonomous unmanned helicopters. Aircraft Engineering and Aerospace Technology. 2011;83(6):375-87. 10.1108/00022661111173252
  35. Du H, Zhu W, Wen G, Wu D. Finite-time formation control for a group of quadrotor aircraft. Aerospace Science and Technology. 2017;69:609-16. 10.1016/j.ast.2017.07.012
  36. Ma’Arif A, Rahmaniar W, Vera MAM, Nuryono AA, Majdoubi R, Çakan A, editors. Artificial potential field algorithm for obstacle avoidance in uav quadrotor for dynamic environment. 2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT); 2021: IEEE. 10.1109/COMNETSAT53002.2021.9530803
International Journal of Engineering
Volume 37, Issue 1
TRANSACTIONS A: Basics
January 2024
Pages 115-126

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