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Iterative MILP methods for vehicle-control problems
| Venue: | IEEE Transactions on Robotics |
| Citations: | 20 - 0 self |
BibTeX
@ARTICLE{Earl_iterativemilp,
author = {Matthew G. Earl and Senior Member},
title = {Iterative MILP methods for vehicle-control problems},
journal = {IEEE Transactions on Robotics},
year = {},
pages = {2005}
}
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Abstract
Abstract—Mixed-integer linear programming (MILP) is a powerful tool for planning and control problems because of its modeling capability and the availability of good solvers. However, for large models, MILP methods suffer computationally. In this paper, we present iterative MILP algorithms that address this issue. We consider trajectory-generation problems with obstacle-avoidance requirements and minimum-time trajectory-generation problems. These problems involve vehicles that are described by mixed logical dynamical equations, a form of hybrid system. The algorithms use fewer binary variables than standard MILP methods, and require less computational effort. Index Terms—Hybrid systems, mathematical optimization, mixed-integer linear programming (MILP), obstacle avoidance, path and trajectory planning, robot motion planning, trajectory generation. I.
Keyphrases
vehicle-control problem iterative milp method computational effort robot motion planning algorithm use binary variable modeling capability good solver control problem trajectory planning index term hybrid system milp method mixed-integer linear programming obstacle-avoidance requirement trajectory-generation problem minimum-time trajectory-generation problem hybrid system large model iterative milp algorithm obstacle avoidance powerful tool mathematical optimization trajectory generation abstract mixed-integer linear programming mixed logical dynamical equation standard milp method
