Ornithpter Trajectory Optimization (OTO) Dataset:

A large data set composed by energy-efficient trajectories obtained by running a competitive planner. To the best of our knowledge, our proposed data set is the first one with a high number of pseudo-optimal paths for ornithopter trajectory optimization. We compare the performance of three methods to compute low-cost trajectories: two classification approaches to learn maneuvers, and an alternative regression method that predicts new states. Random Forest obtains the higher performance with more than 99% and 97% ofaccuracy in a landing and a mid-range scenario, respectively. For more details, please check our publication: (link when available).

In this repository we provide the data, and the algorithms that we use in our proposal. If you find our resuls usefull, or you want to use them in your research, please use the following BibTeX entry:

@article{perez2022ornithopter,
  title={Ornithopter Trajectory Optimization with Neural Networks and Random Forest},
  author={P{\'e}rez-Cuti{\~n}o, MA and Rodr{\'\i}guez, Fabio and Pascual, LD and D{\'\i}az-B{\'a}{\~n}ez, Jos{\'e} Miguel},
  journal={Journal of Intelligent \& Robotic Systems},
  volume={105},
  number={1},
  pages={1--16},
  year={2022},
  publisher={Springer}
}