We propose a method for autonomous navigation of unmanned aerial vehicles (UAVs) in cluttered environment utilizing Laplacian potential field. With suitable boundary conditions for obstacles and goal in the map, Laplace’s equations are solved by boundary element method (BEM). Then we establish artificial potential field which is capable of handling obstacles with complex configuration and guarantees nonexistence of local minima. Since such a kind of potential field has easy access to the gradient of arbitrary point in the map, a linear quadratic Gaussian controller (LQG) is designed for velocity tracking to help complete attitude control. The simulation is conducted on Airsim platform with different scenarioes, and the results illustrate that this method is valid for path planning and obstacle avoidance and has adaptability to unexpected disturbance.
GU Yujin
,
SONG Xiaocheng
,
LIU Xiaopei
,
LU Jie
. Path planning and obstacle avoidance for UAV based on Laplacian potential field[J]. Journal of University of Chinese Academy of Sciences, 2020
, 37(5)
: 681
-687
.
DOI: 10.7523/j.issn.2095-6134.2020.05.013
[1] Pan J, Manocha D. GPU-based parallel collision detection for fast motion planning[J]. The International Journal of Robotics Research, 2012, 31(2):187-200.
[2] Dunias P. Autonomous robots using artificial potential fields[D]. Eindhoven:Technische Universiteit Eindhoven, 1996.
[3] Liang Y, Juntong Q,Dalei S, et al. Survey of robot 3D path planning algorithms[J]. Journal of Control Science and Engineering, 2016, 2016:1-22.
[4] Yershova A, Jaillet L, Simeon T, et al. Dynamic-domain RRTs:efficient exploration by controlling the sampling domain[C]//Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain, 2005:3856-3861.
[5] Karaman S, Frazzoli E. Optimal kinodynamic motion planning using incremental sampling-based methods[C]//49th IEEE Conference on Decision and Control (CDC), Atlanta,GA, 2010:7681-7687.
[6] Khatib O. Real-time obstacle avoidance for manipulators and mobile robots[J]. The International Journal of Robotics Research, 1986, 5(1):90-98.
[7] Yun X, Tan K C. A wall-following method for escaping local minima in potential field based motion planning[C]//19978th International Conference on Advanced Robotics. Proceedings ICAR'97, Monterey, CA, USA, 1997:421-426.
[8] Arambula Cosío F, Padilla Castañeda M A. Autonomous robot navigation using adaptive potential fields[J]. Mathematical & Computer Modelling, 2004, 40(9):1141-1156.
[9] Kim D H, Wang H, Shin S. Decentralized control of autonomous swarm systems using artificial potential functions:analytical design guidelines[C]//IEEE Conference on Decision & Control, Nassau, Bahamas. 2006:159-164.
[10] Connolly C I, Burns J B, Weiss R. Path planning using Laplace's equation[C]//IEEE International Conference on Robotics and Automation, Cincinnati, OH, USA, 1990:2102-2106.
[11] Axler S, Bourdon P, Ramey W. Basic properties of harmonic functions[M]//Harmonic Function Theory. New York:Springer, 2001:1-29.
[12] Bear H S. A new look at the three circles theorem[J]. The American Mathematical Monthly, 1974, 81(5):487-490.
[13] Liu Y. Fast multipole boundary element method:theory and applications in engineering[M]. Cambridge:Cambridge University Press, 2009.
[14] Locatelli A. Optimal control:an introduction[J]. Applied Mechanics Reviews, 2001, 55(3):48-49.
[15] Dorato P, Abdallah C, Cerone V. Linear quadratic control:an introduction[J]. Comment on Circ Res, 2000,87(5):399-405.
[16] Shah S, Dey D, Lovett C, et al. AirSim:high-fidelity visual and physical simulation for autonomous vehicles[C]//Field and Service Robotics. Cham:Springer International Publishing, 2018:621-635.
