Iterative MILP Methods for Vehicle Control Problems

TL;DR

This paper introduces iterative MILP algorithms that reduce computational effort in large vehicle control problems involving obstacle avoidance and minimum time trajectory generation by using fewer binary variables.

Contribution

The paper presents novel iterative MILP algorithms that improve computational efficiency for large-scale vehicle control problems by reducing binary variables needed.

Findings

Algorithms require less computational effort than standard MILP methods.

Fewer binary variables are used in the proposed algorithms.

Effective for trajectory planning with obstacle avoidance and time minimization.

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. The algorithms use fewer binary variables than standard MILP methods and require less computational effort.