OPEN ACCESS
This paper addresses a formation tracking control issue for multiple Unmanned Aerial Vehicles (UAV) flying through a constrained space. Based on the Formation Potential Field (FPF), a Modified Artificial Potential Field (MAPF) technique is proposed, which guarantees to generate and maintain a given formation while avoiding collisions. The technique has two phases. UAVs are gathered around the formation center first during Phase 1, and the formation is then achieved in Phase 2. Furthermore, an obstacle repulsive potential field is introduced into this approach to deal with collision avoidance under environmental constraints. By doing so, discontinuities of the original Formation Potential Field can be avoided, and the stability of formation generation could be enhanced. Simulation results are presented to illustrate the validity of proposed approach.
Formation Control, Collision Avoidance, Artificial Potential Field, UAV.
[1] Casbeer D.W., Li S.M., Beard R.W., McLain T.W.,Mehra R.K. (2005). Forest fire monitoring usingmultiple small UAVs, American Control Conference,Vol. 5, pp. 3530-3535.
[2] Maithripala D.H.A., Jayasuriya S. (2005). Radardeception through phantom track generation, ACC,DOI: 10.1109/ACC.2005.1470620
[3] Khatib O. (1986). Real-time obstacle avoidance formanipulators and mobile robots, The InternationalJournal of Robotics Research, Vol. 5, No. 1, pp. 90-98.DOI: 10.1177/027836498600500106
[4] Laumond J.P. (1998). Robot motion planning andcontrol, Springer. DOI: 10.1007/BFb0036069
[5] Hussien B. (1989). Robot path planning and obstacleavoidance by means of potential function method, Ph.D Dissertation, University of Missouri-Columbia, USA.
[6] Ge S.S., Cui Y.J. (2000). New potential functions formobile robot path planning, IEEE Transactions onRobotics and Automation, Vol. 16, No. 5, pp. 615-620.DOI: 10.1109/70.880813
[7] Yang Y., Wang S., Wu Z., Wang Y. (2011). Motionplanning for multi-HUG formation in an environmentwith obstacles, Ocean Engineering, Vol. 38, No. 17, pp.2262-2269. DOI: 10.1016/j.oceaneng.2011.10.008
[8] Asl A.N., Menhaj M.B., Sajedin A. (2014). Control ofleader-follower formation and path planning of mobilerobots using Asexual Reproduction Optimization(ARO), Applied Soft Computing, Vol. 14, pp. 563-576.
[9] Liu X., Ge S.S., Goh C.H. (2016). Formation potentialfield for trajectory tracking control of multi-agentsinconstrained space, International Journal of Control, pp.1-15.
[10] Wei E., Li T., Hu Y. (2013). Robust adaptive neuralnetwork control for wheeled inverted pendulum withinput saturation, 10th International Symposium onNeural Networks, Dalian. DOI: 10.1007/978-3-642-39068-5_6
[11] Ge S.S., Liu X., Goh C.H., Xu L. (2015). Formationtracking control multi-agents in constrained space,IEEE Transactions on Control Systems Technology,Vol. 24, No. 3, pp. 992-1003.
[12] Chen M., Ge S.S., Choo Y.S. (2009). Neural networktracking control of ocean surface vessels with inputsaturation, The IEEE International Conference onAutomation and Logistics, Shenyang, China. DOI:10.1109/ICAL.2009.5262972
[13 ]Ma H., Wang M., Jia Z., Yang C. (2011). A newframework of optimal multi-robot formation, 30thChinese Control Conference.
[14] Wang X., Yadav V., Balakrishanan S.N. (2007).Cooperative UAV formation flying withobstacle/collision avoidance, IEEE Transactions onControl System Technology, Vol. 15, No. 4, pp. 672-679.DOI: 10.1109/TCST.2007.899191
[15] Luo G., Yu J., Mei Y., Zhang S. (2015). UAV pathplanning in mixed-obstacle environment via artificialpotential field method improved by additional controlforce, Asian Journal of Control, Vol. 17, No. 5, pp.1600-1610.