To overcome the limitations of the conventional artificial potential field (APF) method, which is commonly used for unmanned aerial vehicle (UAV) formation obstacle avoidance control. A novel UAV formation obstacle avoidance control method based on a light transmission model (LTM) and an improved APF method is proposed. First, inspired by the flight of bird flocks, we combine the LTM with an APF function to present an improved APF model which can help UAV find feasible free space to maneuver. From this, UAV can overcome the drawbacks of non-reachable and local minima under the action of LTM. Then, the obstacle avoidance strategy based on the fixed-wing UAV motion model is proposed, and the obstacle avoidance control algorithm for UAV formation is designed. Finally, simulation results show the effectiveness and superiority of the proposed method, which can result in a dramatic improvement in the performance of UAV formation to obstacle avoidance under the complex and non-deterministic environment.
AliZAZhangangH (2021) Multi-unmanned aerial vehicle swarm formation control using hybrid strategy. Transactions of the Institute of Measurement and Control43(12): 2689–2701.
2.
CahyadiADarmawanEDewantoW, et al. (2020) Application of artificial potential field for coverage control with collision avoidance under sensing constraints. Journal of Control, Automation and Electrical Systems31(2): 304–318.
3.
ChangKMaDHanX, et al. (2020) Lyapunov vector-based formation tracking control for unmanned aerial vehicles with obstacle/collision avoidance. Transactions of the Institute of Measurement and Control42(5): 942–950.
4.
ChenSYangZLiuZ, et al. (2018) An improved artificial potential field based path planning algorithm for unmanned aerial vehicle in dynamic environments. In: 2017 international conference on security, pattern analysis, and cybernetics, Shenzhen, China, 15–17 December 2017, pp. 591–596. New York: IEEE.
5.
FuJWenGYuX, et al. (2020) Distributed formation navigation of constrained second-order multiagent systems with collision avoidance and connectivity maintenance. IEEE Transactions on Cybernetics52: 2149–2162.
6.
HaghighiHAsadiDDelahayeD (2021) Multi-objective cooperated path planning of multiple unmanned aerial vehicles based on revisit time. Journal of Aerospace Information Systems18(12): 919–932.
7.
HaghighiHSadatiSHKarimiJ, et al. (2019) A hierarchical and prioritized framework in coordinated maneuver of multiple UAVs based on guidance regulator. Journal of Aerospace Technology and Management 11. Available at: https://www.scielo.br/j/jatm/a/B89H6F6CmtcPrxkYkYjJYqD/?lang=en
8.
HuJZhangHLiuL, et al. (2020) Convergent multiagent formation control with collision avoidance. IEEE Transactions on Robotics36(6): 1805–1818.
9.
HuangSTeoRSHTanKK (2019) Collision avoidance of multi unmanned aerial vehicles: A review. Annual Reviews in Control48: 147–164.
10.
LeeDJeongJKimYH, et al. (2018) An improved artificial potential field method with a new point of attractive force for a mobile robot. In: 2017 2nd international conference on robotics and automation engineering, Shanghai, China, 29–31 December, pp. 63–67. New York: IEEE.
11.
LiDPanZDengH (2020) Two-dimensional obstacle avoidance control algorithm for snake-like robot in water based on immersed boundary-lattice Boltzmann method and improved artificial potential field method. Transactions of the Institute of Measurement and Control42(10): 1840–1857.
12.
LinXWangZQChenXY (2020) Path planning with improved artificial potential field method based on decision tree. In: 27th Saint Petersburg international conference on integrated navigation systems, St. Petersburg, 25–27 May.
13.
LinZYueMChenG, et al. (2021) Path planning of mobile robot with PSO-based APF and fuzzy-based DWA subject to moving obstacles. Transactions of the Institute of Measurement and Control44: 121–132.
14.
LiuRLiuMLiuY (2019) Nonlinear optimal tracking control of spacecraft formation flying with collision avoidance. Transactions of the Institute of Measurement and Control41(4): 889–899.
15.
ManciniMBloiseNCapelloE, et al. (2020) Sliding mode control techniques and artificial potential field for dynamic collision avoidance in Rendezvous Maneuvers. IEEE Control Systems Letters4(2): 313–318.
16.
Orozco-RosasUMontielOSeplvedaR (2019) Mobile robot path planning using membrane evolutionary artificial potential field. Applied Soft Computing Journal77: 236–251.
17.
PanZZhangCXiaY, et al. (2022) An improved artificial potential field method for path planning and formation control of the multi-UAV systems. IEEE Transactions on Circuits and Systems II: Express Briefs69: 1129–1133.
18.
ParkBSYooSJ (2021) Connectivity-maintaining and collision-avoiding performance function approach for robust leader–follower formation control of multiple uncertain underactuated surface vessels. Automatica127: 109501.
19.
QiJGuoJWangM, et al. (2022) Formation tracking and obstacle avoidance for multiple quadrotors with static and dynamic obstacles. IEEE Robotics and Automation Letters7(2): 1713–1720.
20.
RaheemFAAbdulkareemMI (2020) Development of A* algorithm for robot path planning based on modified probabilistic roadmap and artificial potential field. Journal of Engineering Science and Technology15(5): 3034–3054.
21.
ShevitzDPadenB (1994) Lyapunov stability theory of nonsmooth systems. IEEE Transactions on Automatic Control39(9): 1910–1914.
22.
SinglaAPadakandlaSBhatnagarS (2021) Memory-based deep reinforcement learning for obstacle avoidance in UAV with limited environment knowledge. IEEE Transactions on Intelligent Transportation Systems22(1): 107–118.
23.
SongJHaoCSuJ (2020) Path planning for unmanned surface vehicle based on predictive artificial potential field. International Journal of Advanced Robotic Systems17(2): 1–13.
24.
SuiZPuZYiJ, et al. (2021) Formation control with collision avoidance through deep reinforcement learning using model-guided demonstration. IEEE Transactions on Neural Networks and Learning Systems32(6): 2358–2372.
25.
WangDWangPZhangX, et al. (2020) An obstacle avoidance strategy for the wave glider based on the improved artificial potential field and collision prediction model. Ocean Engineering206: 107356.
26.
WuJLuoCLuoY, et al. (2021) Distributed UAV swarm formation and collision avoidance strategies over fixed and switching topologies. IEEE Transactions on Cybernetics. Epub ahead of print 24December. DOI: 10.1109/TCYB.2021.3132587.
27.
WuJWangHLiN, et al. (2020) Formation obstacle avoidance: A fluid-based solution. IEEE Systems Journal14(1): 1479–1490.
28.
YanTXuXLiZ, et al. (2021) Fixed-time leader–following flocking and collision avoidance of multi-agent systems with unknown dynamics. Transactions of the Institute of Measurement and Control43(12): 2734–2741.
29.
YangSBaiWLiT, et al. (2021) Neural-network-based formation control with collision, obstacle avoidance and connectivity maintenance for a class of second-order nonlinear multi-agent systems. Neurocomputing439: 243–255.
30.
YunXTanKC (1997) Wall-following method for escaping local minima in potential field based motion planning. In: International conference on advanced robotics. Proceedings, Monterey, CA, 7–9 July, pp. 421–426. New York: IEEE.
31.
ZappullaRParkHVirgili-LlopJ, et al. (2019) Real-time autonomous spacecraft proximity maneuvers and docking using an adaptive artificial potential field approach. IEEE Transactions on Control Systems Technology27(6): 2598–2605.
