TR2026-064

Invariant-set motion-planner for unicycle dynamics under closed-loop feedback linearization

    •  Leung, J., Di Cairano, S., "Invariant-set motion-planner for unicycle dynamics under closed-loop feedback linearization", American Control Conference (ACC), May 2026.
      BibTeX TR2026-064 PDF
      • @inproceedings{Leung2026may2,
      • author = {Leung, Jordan and {Di Cairano}, Stefano},
      • title = {{Invariant-set motion-planner for unicycle dynamics under closed-loop feedback linearization}},
      • booktitle = {American Control Conference (ACC)},
      • year = 2026,
      • month = may,
      • url = {https://www.merl.com/publications/TR2026-064}
      • }
  • MERL Contacts:
  • Research Areas:

    Control, Dynamical Systems, Robotics

Abstract:

This paper develops an invariant-set motion- planner (ISMP) for vehicles with unicycle-like dynamics. ISMPs operate by determining a sequence of obstacle-free positively invariant (PI) sets for the closed-loop system that connect the initial and target state. In this paper, we derive PI sets under a dynamic feedback linearization law. We demonstrate that the PI sets are simple in geometry and we provide an efficient algorithm for obstacle-free scaling. The PI sets are then used to develop a graph-based search for finding an obstacle-free path between two states. The approach is demonstrated in an obstacle-rich simulated environment.

 

  • Related News & Events

    •  NEWS    MERL researchers present 8 papers at ACC 2026
      Date: May 26, 2026 - May 29, 2026
      Where: New Orleans, USA
      MERL Contacts: Scott A. Bortoff; Vedang M. Deshpande; Stefano Di Cairano; Christopher R. Laughman; Jordan Leung; Hongtao Qiao; Zhaolin Ren; Abraham P. Vinod; Yebin Wang
      Research Areas: Control, Dynamical Systems, Optimization, Robotics
      Brief
      • MERL researchers presented 8 papers at the recently concluded American Control Conference (ACC) 2026 in New Orleans, USA. The papers covered a wide range of topics including robust controllable set computation, vapor compression cycle calibration, task-reasoning LLM agents, Minkowski-cost stable MPC, polynomial chaos approximation, invariant-set motion planning, heat-pump MPC architectures, and relaxed barrier-function MPC. Additionally, Zhaolin Ren was an invited speaker at Multi-Agent Dynamic Games workshop, and Abraham Vinod served as a panelist at the Professional Development and Career Advice for Young Professionals session.

        As a sponsor of the conference, MERL maintained a booth for open discussions with researchers and students, and hosted a special session to discuss highlights of MERL research and work philosophy.
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