Long-term adaptive informative path planning for scalar field monitoring using cross-entropy optimization

This is a preview of subscription content, access via your institution.

References

  1. 1

    Li Z, Huang B, Ye Z, et al. Physical human-robot interaction of a robotic exoskeleton by admittance control. IEEE Trans Ind Electron, 2018, 65: 9614–9624

    Article  Google Scholar 

  2. 2

    Pan Y, Yang C, Pan L, et al. Integral sliding mode control: performance, modification, and improvement. IEEE Trans Ind Inf, 2018, 14: 3087–3096

    Article  Google Scholar 

  3. 3

    Ma K C, Liu L, Heidarsson H K, et al. Data-driven learning and planning for environmental sampling. J Field Robotics, 2018, 35: 643–661

    Article  Google Scholar 

  4. 4

    Yang C, Jiang Y, He W, et al. Adaptive parameter estimation and control design for robot manipulators with finite-time convergence. IEEE Trans Ind Electron, 2018, 65: 8112–8123

    Article  Google Scholar 

  5. 5

    Seeger M. Gaussian processes for machine learning. Int J Neur Syst, 2004, 14: 69–106

    Article  Google Scholar 

  6. 6

    Yang K, Moon S, Yoo S, et al. Spline-based RRT path planner for non-holonomic robots. J Intell Robot Syst, 2014, 73: 763–782

    Article  Google Scholar 

  7. 7

    Reuven R. The cross-entropy method for combinatorial and continuous optimization. Methodol Comput Appl Probabil, 1999, 1: 127–190

    MathSciNet  Article  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. U1813225, 61472325, 61733014, 51579210) and Science, Technology and Innovation Commission of Shenzhen Municipality (Grant No. JCYJ20170817145216803).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Rongxin Cui.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Cui, R., Yan, W. et al. Long-term adaptive informative path planning for scalar field monitoring using cross-entropy optimization. Sci. China Inf. Sci. 62, 50208 (2019). https://doi.org/10.1007/s11432-018-9653-7

Download citation