Skip to main content

Advertisement

Log in

Path Planning Generation Algorithm for a Class of UAV Multirotor Based on State of Health of Lithium Polymer Battery

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

Nowadays, it exists path planning strategies dedicated to generate trajectories considering different navigation issues in UAV multirotors, such as 3D navigation in cluttered and uncluttered environments, obstacle avoidance, and path re-planning. Such path generators are mainly based on the dynamics associated to position and orientation of the UAV, and the attenuation of external disturbances as the wind. However, one of the main limitations of these methods is that they do not take into account the relationship between the path planning task and the energy consumption associated with the battery performance or State of Health (SoH). In this work, a path planning generation algorithm that take into account the evolution of the battery performance is presented. First, the computation of the battery SoH is realized by introducing two degradation models. Subsequently, the path planning algorithm is defined as a multi-objective optimization problem where the objective is to find a feasible trajectory between way-points whiles minimizing the energy consumed and the mission final time depending on the variation of the battery SoH. Finally, the proposed path planning algorithm is compared with a classical path generation method based on polynomial functions to evaluate the minimization of the energy consumption. The simulation results demonstrate that the proposed path planning algorithm is able to generate feasible and minimum energy trajectories despite the constraints in the battery SoH.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Possoch, M., Bieker, S., Hoffmeister, D., Bolten, A., Schellberg, J., Bareth, G.: Multi-temporal crop surface models combined with the RGB vegetation index from UAV-based images for forage monitoring in grassland. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. 41, 991 (2016)

    Article  Google Scholar 

  2. Nex, F., Remondino, F.: UAV For 3D mapping applications: a review. Appl. Geomatics 6(1), 1–15 (2014)

    Article  Google Scholar 

  3. Fernández-Lozano, J., Gutiérrez-Alonso, G.: Improving archaeological prospection using localized UAVs assisted photogrammetry: an example from the Roman Gold District of the Eria River Valley (NW Spain). J. Archaeol. Sci. Rep. 5, 509–520 (2016)

    Google Scholar 

  4. Stek, T.D.: Drones over mediterranean landscapes. The potential of small UAVs (drones) for site detection and heritage management in archaeological survey projects: a case study from Le Pianelle in the Tappino Valley, Molise (Italy). J. Cult. Herit. 22, 1066–1071 (2016)

    Article  Google Scholar 

  5. Nishar, A., Richards, S., Breen, D., Robertson, J., Breen, B.: Thermal infrared imaging of geothermal environments and by an unmanned aerial vehicle (UAV): a case study of the Wairakei-Tauhara geothermal field, Taupo, New Zealand. Renew. Energy 86, 1256–1264 (2016)

    Article  Google Scholar 

  6. Turner, I.L., Harley, M.D., Drummond, C.D.: UAVS for coastal surveying. Coast. Eng. 114, 19–24 (2016)

    Article  Google Scholar 

  7. Casella, E., Rovere, A., Pedroncini, A., Stark, C.P., Casella, M., Ferrari, M., Firpo, M.: Drones as tools for monitoring beach topography changes in the Ligurian Sea (NW Mediterranean). Geo-Mar. Lett. 36(2), 151–163 (2016)

    Article  Google Scholar 

  8. Tulum, K., Durak, U., Yder, S.K.: Situation aware UAV mission route planning. In: 2009 IEEE Aerospace conference, pp. 1–12 (2009)

  9. MacAllister, B., Butzke, J., Kushleyev, A., Pandey, H., Likhachev, M.: Path planning for non-circular micro aerial vehicles in constrained environments. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 3933–3940 (2013)

  10. Bircher, A., Kamel, M., Alexis, K., Oleynikova, H., Siegwart, R.: Receding horizon path planning for 3D exploration and surface inspection. Auton. Robot. 42(2), 291–306 (2018)

    Article  Google Scholar 

  11. Nguyen, P.D., Recchiuto, C.T., Sgorbissa, A.: Real-time path generation and obstacle avoidance for multirotors: a novel approach. J. Intell. Robot. Syst. 89(1-2), 27–49 (2018)

    Article  Google Scholar 

  12. Quionez, Y., Barrera, F., Bugueo, I., Bekios-Calfa, J.: Simulation and path planning for quadcopter obstacle avoidance in indoor environments using the ROS framework. In: International Conference on Software Process Improvement, pp. 295–304 (2017)

  13. Richter, C., Bry, A., Roy, N.: Polynomial trajectory planning for aggressive quadrotor flight in dense indoor environments. In: Inaba M., Corke P. (eds) Robotics Research. Springer Tracts in Advanced Robotics, vol 114. Springer, Cham (2016)

  14. Cheng, F., Hua, W., Pin, C.: Rotorcraft flight endurance estimation based on a new battery discharge model. Chin. J. Aeronaut. 30(4), 1561–1569 (2017)

    Article  Google Scholar 

  15. Broussely, M., Biensan, P., Bonhomme, F., Blanchard, P., Herreyre, S., Nechev, K., Staniewicz, R.: Main aging mechanisms in Li-Ion batteries. J. Power Sources 146(1-2), 90–96 (2005)

    Article  Google Scholar 

  16. Pounds, P., Mahony, R., Gresham, J., Corke, P., Roberts, J.M.: Towards dynamically favourable quad-rotor aerial robots. In: Proceedings of the 2004 Australasian Conference on Robotics & Automation, Australian Robotics & Automation Association (2004)

  17. Driessens, S., Pounds P.E.: Towards a more efficient quadrotor configuration. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1386–1392 (2013)

  18. Verbeke, J., Hulens, D., Ramon, H., Goedeme, T., De Schutter, J.: The design and construction of a high endurance hexacopter suited for narrow corridors. In: 2014 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 543–551 (2014)

  19. Morbidi, F., Cano, R., Lara, D.: Minimum-energy path generation for a quadrotor UAV. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1492–1498 (2016)

  20. Dietrich, T., Krug, S., Zimmermann, A.: An empirical study on generic multicopter energy consumption profiles. In: 2017 Annual IEEE International Systems Conference (SysCon), pp. 1–6 (2017)

  21. Kreciglowa, N., Karydis, K., Kumar, V.: Energy efficiency of trajectory generation methods for stop-and-go aerial robot navigation. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 656–662 (2017)

  22. Chamseddine, A., Zhang, Y., Rabbath, C.A., Join, C., Theilliol, D.: Flatnessbased trajectory planning/replanning for a quadrotor unmanned aerial vehicle. IEEE Trans. Aerosp. Electron. Syst. 48(4), 2832–2848 (2012)

    Article  Google Scholar 

  23. Carrillo, L.R.G., LÓpez, A.E.D., Lozano, R., Pégard, C.: Quad Rotorcraft Control: Vision-Based Hovering and Navigation. Springer, Berlin (2012)

    Google Scholar 

  24. Ortiz-Torres, G., García-Beltrán, C.D., Reyes-Reyes, J., Vidal-Rosas, A., Astorga Zaragoza, C.: Control Tolerante a Fallas Pasivo de un Octorotor tipo X8 utilizando Controladores Backstepping en Cascada, XVI convención de ingeniería eléctrica, CIE, pp. 1–8 (2015)

  25. Gargioli, A., Rinaldi, F., Quagliotti, F.: Proportional Integral Derivative and Linear Quadratic Regulation of a multirotor attitude: mathematical modelling, simulations and experimental results. In: 2013 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 433–442 (2013)

  26. Julien, C., Mauger, A., Vijh, A., Zaghib, K.: Lithium Batteries, pp. 29–68. Springer, Berlin (2016)

    Book  Google Scholar 

  27. Chen, M., Rincon-Mora, G.A.: Accurate electrical battery model capable of predicting runtime and IV performance. IEEE Trans. Energy Conversion 21(2), 504–511 (2006)

    Article  Google Scholar 

  28. Bø, T.I., Johansen, T.A.: Battery power smoothing control in a marine electric power plant using nonlinear model predictive control. IEEE Trans. Control Syst. Technol. 25(4), 1449–1456 (2017)

    Article  Google Scholar 

  29. El-Samahy, A.A., Shamseldin, M.A.: Brushless DC motor tracking control using self-tuning fuzzy PID control and model reference adaptive control. Ain Shams Engineering Journal, In Press, Corrected Proof (2016)

  30. Moseler, O., Isermann, R.: Application of model-based fault detection to a brushless DC motor. IEEE Trans. Ind. Electron. 47(5), 1015–1020 (2000)

    Article  Google Scholar 

  31. Sasaki, T., Ukyo, Y., Novák, P.: Memory effect in a lithium-ion battery. Nat. Mater. 12(6), 569 (2013)

    Article  Google Scholar 

  32. Cordoba-Arenas, A., Onori, S., Rizzoni, G., Fan, G.: Aging propagation in advanced battery systems: preliminary results. IFAC Proc. 46(21), 313–318 (2013)

    Article  Google Scholar 

  33. Haifeng, D., Xuezhe, W., Zechang, S.: A new SoH prediction concept for the power Lithium-Ion battery used on HEVs. In: 2009 IEEE VPPC09 Conference ON Vehicle Power and Propulsion, pp. 1649–1653 (2009)

  34. Saha, B., Goebel, K.: Battery data set NASA AMES prognostics data repository (2007)

  35. Corke, P.: Robotics, Vision and Control: Fundamental Algorithms In MATLAB®; Second Completely Revised, vol. 118. Springer, Berlin (2017)

    Book  Google Scholar 

  36. Tokekar, P., Karnad, N., Isler, V.: Energy-optimal trajectory planning for car-like robots. Auton. Robot. 37(3), 279–300 (2014)

    Article  Google Scholar 

  37. Patterson, M.A., Rao, A.V.: GPOPS-II: a MATLAB software for solving multiple-phase optimal control problems using hp-adaptive gaussian quadrature collocation methods and sparse nonlinear programming. ACM Transactions on Mathematical Software (TOMS) 41(1), 1 (2014)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

Ricardo Schacht Rodríguez acknowledges the economic support provided by Consejo Nacional de Ciencia y Tecnología (CONACyT) through doctoral scholarship program.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to J.-C. Ponsart.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was supported by CONACyT (Consejo Nacional de Ciencia y Tecnología.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schacht-Rodríguez, R., Ponsart, JC., García-Beltrán, CD. et al. Path Planning Generation Algorithm for a Class of UAV Multirotor Based on State of Health of Lithium Polymer Battery. J Intell Robot Syst 91, 115–131 (2018). https://doi.org/10.1007/s10846-018-0870-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-018-0870-0

Keywords

Navigation