Improving Real-Time Energy-Efficient Trajectory Planning Via Machine Learning


This research demonstrates benefits from leveraging simple machine learning algorithms to generate a computationally light, suitably tailored heuristic function to enable mobile robots to make faster, more accurate decisions in navigation tasks. This can be achieved by collecting data from simulations and training top-performing algorithms like Light GBM, MLP, Ridge Regressor, etc., into a heuristic learning model. Our approach is anticipated to replace existing naive heuristic functions by reducing computation time to solution and targeting exploration.

See publication:
This publication pertains to:
Systems of Systems
Publication Authors:
  • Carter Bailey
  • Mario Harper
It appeared in:
Peer-reviewed conference proceedings
Training , Machine learning algorithms , Trajectory planning , Navigation , Computational modeling , Machine learning , Data models