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A Harmonic Potential Approach for Simultaneous Planning and Control of a Generic UAV Platform

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Abstract

Simultaneous planning and control of a large variety of unmanned aerial vehicles (UAVs) is tackled using the harmonic potential field (HPF) approach. A dense reference velocity field generated from the gradient of an HPF is used to regulate the velocity of the UAV concerned in a manner that would propel the UAV to a target point while enforcing the constraints on behavior that were a priori encoded in the reference field. The regulation process is carried-out using a novel and simple concept called the: virtual velocity attractor (VVA). The combined effect of the HPF gradient and the VVA is found able to yield an efficient, easy to implement, well-behaved and provably-correct context-sensitive control action that suits a wide variety of UAVs. The approach is developed and basic proofs of correctness are provided along with simulation results.

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References

  1. Jones, C.: Unmanned aerial vehicles (UAVS) an assessment of historical operations and future posibilities. In: Paper presented to the research department air command and staff college in partial fulfillment of the graduation requirements of ACSC USAF, AU/ACSC/0230D/97-03 (1997)

  2. Quaritsch, M., Kruggl, K., Wischounig-Strucl, D., Bhattacharya, S., Shah, M., Rinner, B.: Networked UAVs as aerial sensor network for disaster management applications. Elektrotechnik & Informationstechnik 127(2), 56–63 (2010)

    Article  Google Scholar 

  3. Lomax, A.S., Corso, W., Etro, J.F.: Employing unmanned aerial vehicles (UAVs) as an element of the integrated ocean observing system, OCEANS. In: Proceedings of MTS/IEEE, vol. 1, pp. 184–190 (2005)

  4. Chen, H., Wang, X., Li, Y.: A survey of autonomous control for UAV. In: International Conf. on Artificial Intelligence and Computational Intelligence, pp. 267–271. Las Vegas, USA, 13–16 July 2009

  5. Goerzen, C., Kong, Z., Mettler, B.: A survey of motion planning algorithms from the perspective of autonomous UAV guidance. J. Intell. Robot. Syst. 57, 65–100 (2010)

    Article  MATH  Google Scholar 

  6. Chao, H., Cao, Y., Chen, Y.: Autopilots for small unmanned aerial vehicles: a survey. Int. J. Control Autom. Syst. 8(1), 36–44 (2010)

    Article  Google Scholar 

  7. Ren, W.: On constrained nonlinear tracking control of a small fixed-wing UAV. J. Intell. Robot. Syst. 48(3), 525–537 (2007)

    Article  Google Scholar 

  8. Roberts, P.J., Walker, R.A.: Fixed wing UAV navigation and control through integrated GNSS and vision. In: AIAA Guidance, Navigation, and Control Conference and Exhibit, pp. 1–13. San Francisco, California (2005)

  9. Suresh, S., Kannan N.: Direct adaptive neural flight control system for an unstable unmanned aircraft. Appl. Soft Comput. 8, 937–948 (2008)

    Article  Google Scholar 

  10. Astrov, I., Pedai, A.: Control of hovering manoeuvres in unmanned helicopter for enhanced situational awareness. In: International Conference on Industrial Mechatronics and Automation, pp. 143–146 (2009)

  11. Brown, A., Garcia, R.: Concepts and validation of a small-scale rotorcraft proportional integral derivative (PID) controller in a unique simulation environment. J. Intell. Robot Syst. 54, 511–532 (2009)

    Article  Google Scholar 

  12. Cai, G., Cai, A.K., Chen, B.M., Lee, T.H.: Construction, modeling and control of a mini autonomous UAV helicopter. In: Proceedings of the IEEE International Conference on Automation and Logistics Qingdao, pp. 449–454. China (2008)

  13. Dierks, T., Jagannathan, S.: Output feedback control of a Quadrotor UAV using neural networks. IEEE Trans. Neural Netw. 21(1), 50–66 (2010)

    Article  Google Scholar 

  14. Kim, J., Kang, M., Park, S.: Accurate modeling and robust hovering control for a Quad–rotor VTOL aircraft. J. Intell. Robot Syst. 57, 9–26 (2010)

    Article  MATH  Google Scholar 

  15. Bouabdallah, S., Siegwart, R.: Full control of a Quadrotor. In: Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems San Diego, pp. 153–158. CA, USA (2007)

  16. Lee, J., Min, B., Kim, E.: Autopilot design of tilt-rotor UAV using particle swarm optimization method. In: International Conference on Control, Automation and Systems, pp. 1629–1633. COEX, Seoul, Korea, 17–20 Oct 2007

  17. Yanguo, S., Huanjin, W.: Design of flight control system for a small unmanned tilt rotor aircraft. Chin. J. Aeronauti. 22, 250–256 (2009)

    Article  Google Scholar 

  18. Escareno, J., Sanchez, A., Garcia, O., Lozano, R.: Triple tilting rotor mini-UAV: modeling and embedded control of the attitude. In: American Control Conference Westin Seattle Hotel, pp. 3476–3481. Seattle (2008)

  19. Liu, Y., Pan, Z., Stirling, D., Naghdy, F.: Control of autonomous airship. In: Proceedings of the 2009 IEEE International Conference on Robotics and Biomimetics. pp. 2457–2462. Guilin, China (2009)

    Chapter  Google Scholar 

  20. Knoebel, N.B., McLain, T.W.: Adaptive quaternion control of a miniature tailsitter UAV. In: American Control Conference Westin Seattle Hotel, pp. 2340–2345. Seattle, Washington, USA, 11–13 June 2008

  21. Watanabe, M., Ochi, Y.: Modeling and motion analysis for a powered paraglider (PPG), SICE, 2007, pp. 3007–3012. Kagawa University, Japan (2007)

    Google Scholar 

  22. Leven, S., Zufferey J., Floreano D.: A minimalist control strategy for small UAVs. In: The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2873–2878. St. Louis, USA (2009)

  23. Xu, Y.: Nonlinear robust stochastic control for unmanned aerial vehicles. In: 2009 American Control Conference Hyatt Regency Riverfront, pp. 2819–2824. St. Louis, MO, USA (2009)

  24. Shehab, S., Rodrigues, L.: Preliminary results on UAV path following using piecewise-affine control. In: Proceedings of the 2005 IEEE Conference on ControlApplications, pp. 358–363. Toronto, Canada (2005)

  25. Kakirde, N.P., Davari, A., Wang, J.: Trajectory tracking of unmanned aerial vehicle using servomechanism strategy. In: Proceedings of the Thirty-Seventh Southeastern Symposium on System Theory, SSST ‘05, pp. 163–166, 20–22 Mar 2005

  26. Lei, X., Liang, J., Wang, S., Wang, T.: An integrated navigation system for a small UAV using low-cost sensors. In: Proceedings of the 2008 IEEE International Conference on Information and Automation Zhangjiajie, pp. 765–769. China, 20–23 June 2008

  27. McInnes, C.: Velocity field path-planning for single and multiple unmanned aerial vehicles. Aeronaut. J. 107(1073), 419–426 (2003)

    Google Scholar 

  28. Yang, K., Gan, S., Sukkarieh, S.: An efficient path planning and control algorithm for RUAV’s in unknown and cluttered environments. J. Intell. Robot. Syst. 57, 101–122 (2010)

    Article  MATH  Google Scholar 

  29. Jun, J., Pei-bei, M., Dan, L., Xiao-jie, Z.: Time control based on three-dimensional dynamic path planning. In: Proceedings of the IEEE International Conference on Automation and Logistics Shenyang, pp. 1832–1837. China (2009)

  30. Yu, J., Xu, Q., Zhao, G., Zhang, R.: A scheme of integrated guidance/autopilot design for UAV based on TSM control. In: 2007 IEEE International Conference on Control and Automation WeDP-4 Guangzhou, pp. 707–711. China (2007)

  31. Pastor, E., Royo, J.: A hardware/software architecture for UAV payload and mission control. In: 25th Digital Avionics Systems Conference, IEEE/AIAA, pp. 5B4-1–5B4-8, 15–19 Oct 2006

  32. Hameed, T., Zhang, W.: Conceptual designing—unmanned aerial vehicle flight control system. In: The 9th International Conference on Electronic Measurement & Instruments (ICEMI) will be held on 16–18 August, vol. 3, Beijing, China, pp. 712–716 (2009)

  33. Connolly, C., Weiss, R., Burns, J.: Path planning using laplace equation. In: IEEE Int. Conf. Robotics Automat., Cincinnati, OH, pp. 2102–2106 (1990)

  34. Akashita, S., Kawamura, S., Hayashi, K.: New navigation function utilizing hydrodynamic potential for mobile robots. In: IEEE Int. Workshop Intelli. Motion Contr., Istanbul, Turkey, pp. 413–417 (1990)

  35. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: IEEE Int. Conf. Robotics and Automation, St. Louis, MO, pp. 500–505 (1985)

  36. Masoud, A.: An informationally-open, organizationally-closed control structure for navigating a robot in an unknown, stationary environment. In: 2003 IEEE International Symposium on Intelligent Control, Houston, Texas, USA, pp. 614–619 (2003)

  37. Masoud, S., Masoud, A.: Motion planning in the presence of directional and obstacle avoidance constraints using nonlinear anisotropic, harmonic potential fields: a physical metaphor. IEEE Trans. Syst. Man Cybern., Part A, Syst. Humans 32(6), 705–723 (2002)

    Google Scholar 

  38. Masoud, A.: A harmonic potential field approach with a probabilistic space descriptor for planning in non-divisible environments. In: IEEE International Conference on Robotics and Automation, Kobe, Japan, pp. 3774–3779, 12–17 May 2009

  39. Masoud, A.: Planning with gamma-harmonic functions. In: 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Montreal, Canada, 6–9 July 2010

  40. Gupta, R., Masoud, A., Chow, M.: A network based, delay-tolerant, integrated navigation system for a differential drive UGV using harmonic potential field. In: Proceedings of the 45th IEEE Conference on Decision & Control Manchester Grand Hyatt Hotel, San Diego, CA, USA, pp. 1870–1875, 13–15 Dec 2006

  41. Masoud, S., Masoud, A.: Constrained motion control using vector potential fields. IEEE Trans. Syst. Man Cybern., Part A, Syst. Humans 30(2), 251–272 (2000)

    Article  Google Scholar 

  42. Masoud, A.: Kinodynamic motion planning: a novel type of nonlinear, passive damping forces and advantages. IEEE Robot. Autom. Mag. 17(1), 85–99 (2010)

    Article  Google Scholar 

  43. Masoud, A.: A harmonic potential field approach for navigating a rigid, nonholonomic robot in a cluttered environment. In: IEEE International Conference on Robotics and Automation, Kobe, Japan, pp. 3993–3999 (2009)

  44. Joshi, V.A., Banavar, R.N.: Motion analysis of a spherical mobile robot. Robotica 27, 343–353. Cambridge University Press (2009)

    Article  Google Scholar 

  45. Nahon, M.: A simplified dynamics model for autonomous underwater vehicles. In: Proc 1996 Symp. on Autonomous Underwater Vehicle Technology, New York, pp. 373–379 (1996)

  46. Vinh, N.: Flight Mechanics of High-Performance Aircraft. Cambridge University Press (1995)

  47. Masoud, A.: Decentralized, self-organizing, potential field-based control for individually-motivated, mobile agents in a cluttered environment: a vector-harmonic potential field approach. IEEE Trans. Syst. Man Cybern., Part A, Syst. Humans 37(2), 372–390 (2007)

    Article  Google Scholar 

  48. Etkin, E.: Dynamics of Flight, 2nd edn. Wiley, New York (1982)

    Google Scholar 

  49. LaSalle, J.: Some extensions of Lyapunov’s second method. IRE Trans. Circuit Theory CT-7(3), 520–527 (1960)

    MathSciNet  Google Scholar 

  50. Masoud, A.: A virtual velocity attractor, harmonic potential approach for joint planning and control of a UAV. In: American Control Conference 2011, San Francesco, 29 June–1 July 2011

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Masoud, A.A. A Harmonic Potential Approach for Simultaneous Planning and Control of a Generic UAV Platform. J Intell Robot Syst 65, 153–173 (2012). https://doi.org/10.1007/s10846-011-9570-8

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