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Summary
This summary is machine-generated.

This study compares data-driven methods to speed up robot motion planning using mixed-integer bilinear programs (MIBLPs). McCormick envelope-based mixed-integer programming (MICP) offers fast real-time solutions with ample data, while model predictive control (MPC) excels with limited data.

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mathematical programs with complementarity constraintsmixed-integer bilinear programsoptimal controlrobot motion planningtrajectory optimization

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Area of Science:

  • Robotics
  • Optimization
  • Artificial Intelligence

Background:

  • Mixed-integer bilinear programs (MIBLPs) are computationally intensive for real-time robot motion planning.
  • Existing methods struggle with the combined discrete and nonlinear nature of MIBLPs.

Purpose of the Study:

  • To investigate data-driven acceleration techniques for MIBLPs in robot motion planning.
  • To compare the effectiveness of two MIBLP reformulation strategies: McCormick envelopes (MICP) and complementarity constraints (MPCC).

Main Methods:

  • Reformulating MIBLPs into MICP using McCormick envelopes.
  • Reformulating MIBLPs into MPCC by converting binary variables to continuous ones.
  • Employing K-nearest neighbor methods for data-driven warm-starting of both reformulations.

Main Results:

  • MICP demonstrates consistent, real-time suitable solving speeds with sufficient data.
  • MPCC achieves higher success rates when limited data is available.
  • The approach successfully planned complex motions for the SCALER robot, including gait transitions.

Conclusions:

  • Data-driven warm-starting significantly enhances MIBLP solvers for robot motion planning.
  • The choice between MICP and MPCC depends on data availability for achieving optimal performance.