Energy-Efficient Path Planning for Solar-Powered Mobile Robots

  • Patrick A. Plonski
  • Pratap Tokekar
  • Volkan Isler
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 88)


We explore the problem of energy-efficient, time-constrained path planning of a solar powered robot embedded in a terrestrial environment. Because of the effects of changing weather conditions, as well as sensing concerns in complex environments, a new method for solar power prediction is desired. We present a method that uses Gaussian Process regression to build a solar map in a data-driven fashion. With this map, we perform energy-optimal path planning using a dynamic programming algorithm. We validate our map construction and path planning algorithms with outdoor experiments, and perform simulations on our solar maps to determine under which conditions the weight of added solar panels is worthwhile for a mobile robot.


Mobile Robot Covariance Function Path Planning Solar Panel Solar Power 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Carsten, J., Rankin, A., Ferguson, D., Stentz, A.: Global path planning on board the mars exploration rovers. In: 2007 IEEE Aerospace Conference, pp. 1–11 (March 2007)Google Scholar
  2. 2.
    Derenick, J., Michael, N., Kumar, V.: Energy-aware coverage control with docking for robot teams. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3667–3672 (September 2011)Google Scholar
  3. 3.
    Ferguson, D., Stentz, A.: Using Interpolation to Improve Path Planning: The Field D * Algorithm. Journal of Field Robotics 23(2), 79–101 (2006)zbMATHCrossRefGoogle Scholar
  4. 4.
    Goswami, D.Y., Kreith, F., Kreider, J.F.: Principles of Solar Engineering, 2nd edn. Taylor & Francis (1999)Google Scholar
  5. 5.
    Jensen, E., Franklin, M., Lahr, S., Gini, M.: Sustainable multi-robot patrol of an open polyline. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 4792–4797. IEEE (2011)Google Scholar
  6. 6.
    Kim, C., Kim, B.: Minimum-energy translational trajectory generation for differential-driven wheeled mobile robots. Journal of Intelligent & Robotic Systems 49(4), 367–383 (2007)CrossRefGoogle Scholar
  7. 7.
    Liu, S., Sun, D.: Optimal motion planning of a mobile robot with minimum energy consumption. In: 2011 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 43–48 (July 2011)Google Scholar
  8. 8.
    Mei, Y.: Energy-Efficient Mobile Robots. PhD thesis, Purdue University (2006)Google Scholar
  9. 9.
    Rasmussen, C., Williams, C.: Gaussian processes in machine learning. MIT Press (2006)Google Scholar
  10. 10.
    Ray, L., Lever, J., Streeter, A., Price, A.: Design and Power Management of a Solar-Powered Cool Robot for Polar Instrument Networks. Journal of Field Robotics 24(7), 581–599 (2007)CrossRefGoogle Scholar
  11. 11.
    Sauze, C., Neal, M.: Long term power management in sailing robots. In: 2011 IEEE - OCEANS, Spain, pp. 1–8 (June 2011)Google Scholar
  12. 12.
    Sugihara, R., Gupta, R.: Optimizing energy-latency trade-off in sensor networks with controlled mobility. In: IEEE INFOCOM 2009, pp. 2566–2570. IEEE (2009)Google Scholar
  13. 13.
    Sun, Z., Reif, J.: On finding energy-minimizing paths on terrains. IEEE Transactions on Robotics 21(1), 102–114 (2005)CrossRefGoogle Scholar
  14. 14.
    Tekdas, O., Bhadauria, D., Isler, V.: Efficient Data Collection from Wireless Nodes under the Two-Ring Communication Model. The International Journal of Robotics Research 31(6), 774–784 (2012)CrossRefGoogle Scholar
  15. 15.
    Tokekar, P., Karnad, N., Isler, V.: Energy-optimal velocity profiles for car-like robots. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 1457–1462. IEEE (2011)Google Scholar
  16. 16.
    Tompkins, P., Stentz, A., Wettergreen, D.: Mission-level path planning and re-planning for rover exploration. Robotics and Autonomous Systems 54(2), 174–183 (2006)CrossRefGoogle Scholar
  17. 17.
    Wang, G., Irwin, M., Fu, H., Berman, P., Zhang, W., Porta, T.: Optimizing sensor movement planning for energy efficiency. ACM Transactions on Sensor Networks (TOSN) 7(4), 33 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Patrick A. Plonski
    • 1
  • Pratap Tokekar
    • 1
  • Volkan Isler
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of MinnesotaMinneapolisUSA

Personalised recommendations