TR2022-109

Motion Planning and Model Predictive Control for Automated Tractor-Trailer Hitching Maneuver

    •  Wang, Z., Ahmad, A., Quirynen, R., Wang, Y., Bhagat, A., Zeino, E., Zushi, Y., Di Cairano, S., "Motion Planning and Model Predictive Control for Automated Tractor-Trailer Hitching Maneuver", IEEE Conference on Control Technology and Applications (CCTA), DOI: 10.1109/​CCTA49430.2022.9966181, August 2022, pp. 676-682.
      BibTeX TR2022-109 PDF
      • @inproceedings{Wang2022aug,
      • author = {Wang, Zejiang and Ahmad, Ahmad and Quirynen, Rien and Wang, Yebin and Bhagat, Akshay and Zeino, Eyad and Zushi, Yuji and Di Cairano, Stefano},
      • title = {Motion Planning and Model Predictive Control for Automated Tractor-Trailer Hitching Maneuver},
      • booktitle = {IEEE Conference on Control Technology and Applications (CCTA)},
      • year = 2022,
      • pages = {676--682},
      • month = aug,
      • publisher = {IEEE},
      • doi = {10.1109/CCTA49430.2022.9966181},
      • url = {https://www.merl.com/publications/TR2022-109}
      • }
  • MERL Contacts:
  • Research Areas:

    Control, Dynamical Systems, Optimization, Robotics

Abstract:

In recent years significant progress has been made in optimization-based planning and control for automated vehicle operation. For heavy-duty vehicles, the research focus has been on platooning and control of articulated vehicles especially when cruising on the highway. This paper proposes an integrated system using a motion planning algorithm and a real-time reference tracking controller, tailored to the task of automated tractor-trailer hitching which is a critical maneuver in heavy-duty vehicle operations, due to requiring a very high precision. The motion planner is based on a bi-directional A- search guided tree algorithm and the tracking controller is implemented using nonlinear model predictive control. To validate the proposed approach, we present results from hardware-in- the-loop simulations on a dSPACE Scalexio real-time computing unit and extensive Monte Carlo closed-loop simulations.