UAV Formation and Obstacle Avoidance Based on Improved APF

This paper put forward a solution for fixed-wing UAV formation control and obstacles avoidance based on an improved artificial potential field method (APF) and leader-follower structure. First, simplified model of fixed-wing UAV with kinematic constraints is introduced. Subsequently an improved APF considering the kinematic constraints and formation configuration is proposed. Then, a formation transformation method based on improved APF is put forward to increase the flexibility and reliability of formation to avoid obstacles in environment, which means the feedback of obstacle information to formation controller. Finally, simulations of UAV formation, tracking the desired trajectory and obstacle avoidance is presented and the results verify the effectiveness of the improved APF method.

[1]  Zhou Chao,et al.  UAV Formation Flight Based on Nonlinear Model Predictive Control , 2012 .

[2]  Haibin Duan,et al.  Pigeon-Inspired optimization approach to multiple UAVs formation reconfiguration controller design , 2014, Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference.

[3]  Shuo Zhang,et al.  Small morphing wing aerial vehicle dynamic modelling basing on simulation and flight test , 2017, Int. J. Model. Identif. Control..

[4]  Guangming Xie,et al.  Leader-following formation control of multiple mobile vehicles , 2007 .

[5]  R. K. Barai,et al.  Leader-follower formation control using artificial potential functions: A kinematic approach , 2012, IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012).

[6]  Jie Zhang,et al.  UAV formation control with obstacle avoidance using improved artificial potential fields , 2017, 2017 36th Chinese Control Conference (CCC).

[7]  Huosheng Hu,et al.  A novel reconfigurable control method for an aircraft with potential actuator failures , 2017, Int. J. Comput. Appl. Technol..

[8]  Xiong Jun Application Areas and Future of UAV , 2010 .

[9]  Youmin Zhang,et al.  Sense and avoid technologies with applications to unmanned aircraft systems: Review and prospects , 2015 .

[10]  Hugh H. T. Liu,et al.  Formation UAV flight control using virtual structure and motion synchronization , 2008, 2008 American Control Conference.

[11]  Jie Wang,et al.  Adaptive consensus of scale-free multi-agent systems with event-triggered communications , 2017, Int. J. Model. Identif. Control..

[12]  Jie Wang,et al.  Adaptive consensus of scale-free multi-agent systems with event-triggered communications , 2017 .

[13]  Fuchun Sun,et al.  Decentralized UAV formation tracking flight control using gyroscopic force , 2009, 2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications.

[14]  Reza Olfati-Saber,et al.  Flocking for multi-agent dynamic systems: algorithms and theory , 2006, IEEE Transactions on Automatic Control.

[15]  Seungkeun Kim,et al.  Three dimensional optimum controller for multiple UAV formation flight using behavior-based decentralized approach , 2007, 2007 International Conference on Control, Automation and Systems.

[16]  Scott A. Smolka,et al.  A survey on unmanned aerial vehicle collision avoidance systems , 2015, ArXiv.