Unsupervised learning-based solution of the Close Enough Dubins Orienteering Problem

@article{Faigl2019UnsupervisedLS,
  title={Unsupervised learning-based solution of the Close Enough Dubins Orienteering Problem},
  author={Jan Faigl},
  journal={Neural Computing and Applications},
  year={2019},
  pages={1-19}
}
This paper reports on the application of novel unsupervised learning-based method called the Growing Self-Organizing Array (GSOA) to data collection planning with curvature-constrained paths that is motivated by surveillance missions with aerial vehicles. The planning problem is formulated as the Close Enough Dubins Orienteering Problem which combines combinatorial optimization with continuous optimization to determine the most rewarding data collection path that does not exceed the given… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-10 OF 38 REFERENCES

International workshop on self-organizing maps and learning vector quantization, clustering and data visualization (WSOM?)

J Faigl
  • 2017
VIEW 11 EXCERPTS
HIGHLY INFLUENTIAL

Pěnička R (2017) IEEE/RSJ international conference on intelligent robots and systems (IROS), pp

J Faigl
  • 2017
VIEW 10 EXCERPTS
HIGHLY INFLUENTIAL

On the Dubins Traveling Salesman Problem

  • IEEE Transactions on Automatic Control
  • 2012
VIEW 9 EXCERPTS
HIGHLY INFLUENTIAL

Váňa P (2017) International joint conference on neural networks (IJCNN), pp

J Faigl
  • 2017
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

Váňa P (2016) International conference on artificial neural networks (ICANN), pp

J Faigl
  • 2016
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL