TR2022-133

Improved A-Search Guided Tree for Autonomous Trailer Planning


    •  Leu, J., Wang, Y., Tomizuka, M., Di Cairano, S., "Improved A-Search Guided Tree for Autonomous Trailer Planning", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2022.
      BibTeX TR2022-133 PDF
      • @inproceedings{Leu2022oct,
      • author = {Leu, Jessica and Wang, Yebin and Tomizuka, Masayoshi and Di Cairano, Stefano},
      • title = {Improved A-Search Guided Tree for Autonomous Trailer Planning},
      • booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
      • year = 2022,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2022-133}
      • }
  • MERL Contacts:
  • Research Areas:

    Artificial Intelligence, Control, Optimization

Abstract:

This paper presents a motion planning strategy that utilizes the improved A-search guided tree to enable autonomous parking of a general 3-trailer with a car-like tractor. Different from the state-of-the-art state-lattice-based methods where numerous motion primitives are necessary to ensure successful planning, our work allows quick off-lattice exploration to find a solution. Our treatment brings at least three advantages: fewer and lower design complexity of motion primitives, improved success rate, and increased path quality. Unlike on-lattice exploration, where the cost-to-go is obtained by querying a heuristic look-up table, off-lattice exploration entails the heuristic function being well-defined at off-lattice nodes. We train a neural network through reinforcement learning to model the maneuver costs of the trailer and use it as the heuristic value to better approximate the cost-to- go. Simulations demonstrate the effectiveness of the proposed method in terms of planning speed and path length.