Verifiable control of a swarm of unmanned aerial vehicles

Abstract This article considers the distributed control of a swarm of unmanned aerial vehicles (UAVs) investigating autonomous pattern formation and reconfigurability. A behaviour-based approach to formation control is considered with a velocity field control algorithm developed through bifurcating potential fields. This new approach extends previous research into pattern formation using potential field theory by considering the use of bifurcation theory as a means of reconfiguring a swarm pattern through a free parameter change. The advantage of this kind of system is that it is extremely robust to individual failures, is scalable, and also flexible. The potential field consists of a steering and repulsive term with the bifurcation of the steering potential resulting in a change of the swarm pattern. The repulsive potential ensures collision avoidance and an equally spaced final formation. The stability of the system is demonstrated to ensure that desired behaviours always occur, assuming that at large separation distances the repulsive potential can be neglected through a scale separation that exists between the steering and repulsive potential. The control laws developed are applied to a formation of ten UAVs using a velocity field tracking approach, where it is shown numerically that desired patterns can be formed safely ensuring collision avoidance.

[1]  C. Bil,et al.  Control design for unmanned aerial vehicle swarming , 2008 .

[2]  R. Kalman,et al.  Control system analysis and design via the second method of lyapunov: (I) continuous-time systems (II) discrete time systems , 1959 .

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

[4]  Aníbal Ollero,et al.  Control and perception techniques for aerial robotics , 2003, Annu. Rev. Control..

[5]  Shuzhi Sam Ge,et al.  Dynamic Motion Planning for Mobile Robots Using Potential Field Method , 2002, Auton. Robots.

[6]  D. W. Jordan,et al.  Nonlinear ordinary differential equations : an introduction to dynamical systems , 1999 .

[7]  Timothy W. McLain,et al.  Autonomous Vehicle Technologies for Small Fixed Wing UAVs , 2003 .

[8]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[9]  Randal W. Beard,et al.  Target Acquisition, Localization, and Surveillance Using a Fixed-Wing Mini-UAV and Gimbaled Camera , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[10]  Tucker R. Balch,et al.  Behavior-based formation control for multirobot teams , 1998, IEEE Trans. Robotics Autom..

[11]  C. McInnes,et al.  On-orbit assembly using superquadric potential fields , 2008 .

[12]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[13]  Colin R. McInnes,et al.  Robot Motion Planning using Hyperboloid Potential Functions , 2007, World Congress on Engineering.

[14]  Colin R. McInnes.  Velocity field path-planning for single and multiple unmanned ariel vehicles , 2003 .

[15]  R. E. Kalman,et al.  Control System Analysis and Design Via the “Second Method” of Lyapunov: I—Continuous-Time Systems , 1960 .

[16]  G. Gowtham,et al.  Simulation of multi UAV flight formation , 2005, 24th Digital Avionics Systems Conference.

[17]  A. Bertozzi,et al.  Self-propelled particles with soft-core interactions: patterns, stability, and collapse. , 2006, Physical review letters.

[18]  Frank McQuade.  Autonomous control for on-orbit assembly using artificial potential functions , 1997 .

[19]  Hongyan Wang,et al.  Social potential fields: A distributed behavioral control for autonomous robots , 1995, Robotics Auton. Syst..

[20]  Jonathan P. How,et al.  UAV Trajectory Design Using Total Field Collision Avoidance , 2003 .

[21]  J. Redi,et al.  Coordinated flocking of UAVs for improved connectivity of mobile ground nodes , 2004, IEEE MILCOM 2004. Military Communications Conference, 2004..

[22]  Eric W. Frew,et al.  Coordinated Standoff Tracking of Moving Targets Using Lyapunov Guidance Vector Fields , 2008 .

[23]  Eric W. Frew,et al.  Lyapunov Vector Fields for Autonomous Unmanned Aircraft Flight Control , 2008 .

[24]  Tamer Basar.  An Invariance Principle in the Theory of Stability , 2001 .

[25]  Dario Izzo,et al.  Autonomous and Distributed Motion Planning for Satellite Swarm , 2007 .

[26]  C.J. Tomlin,et al.  Automated multiple UAV flight - the Stanford DragonFly UAV Program , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[27]  Timothy W. McLain,et al.  Autonomous Vehicle Technologies for Small Fixed-Wing UAVs , 2003, J. Aerosp. Comput. Inf. Commun..

[28]  Kar-Han Tan,et al.  Virtual structures for high-precision cooperative mobile robotic control , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

[29]  Derek James Bennet,et al.  Pattern transition in spacecraft formation flying via the artificial potential field method and bifurcation theory , 2008 .

[30]  Kyuseung Han,et al.  Unmanned aerial vehicle swarm control using potential functions and sliding mode control , 2008 .

[31]  吉澤 太郎.  An Invariance Principle in the Theory of Stability (常微分方程式及び函数微分方程式研究会報告集) , 1968 .

[32]  William Crowther,et al.  Rule-based guidance for flight vehicle flocking , 2004 .