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 introduce two iterative MILP algorithms that address this issue. The first is for obstacle avoidance problems, and the second is for minimum time optimal control problems. The algorithms require fewer binary variables than standard MILP methods and on average require much less computational effort.
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