Distance metric approximation for state-space RRTs using supervised learning

@article{Bharatheesha2014DistanceMA,
  title={Distance metric approximation for state-space RRTs using supervised learning},
  author={Mukunda Bharatheesha and Wouter Caarls and Wouter J. Wolfslag and Martijn Wisse},
  journal={2014 IEEE/RSJ International Conference on Intelligent Robots and Systems},
  year={2014},
  pages={252-257}
}
The dynamic feasibility of solutions to motion planning problems using Rapidly Exploring Random Trees depends strongly on the choice of the distance metric used while planning. The ideal distance metric is the optimal cost of traversal between two states in the state space. However, it is computationally intensive to find the optimal cost while planning. We propose a novel approach to overcome this barrier by using a supervised learning algorithm that learns a nonlinear function which is an… CONTINUE READING
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