mixed integer linear programming

In this paper, we integrate model predictive control (MPC) and mixed integer linear programming (MILP) into a hierarchical framework suitable for solving optimization problems involving robotic networks. A critical issue in MPC/MILP applications is that the underlying optimization problem must be solved on-line. This creates a time constraint which is hard to meet when the number of robots and the number of obstacles increase. To alleviate this difficulty, we develop strategies that significantly improve the efficiency of a hierarchical, decentralized optimization scheme. As an application is considered a case of target assignment problem in urban-like environments. Numerical simulations verify the scalability of the algorithm to the number of robots and complexity of the environment.

In this paper, we combine model predictive control (MPC) and mixed integer linear programming (MILP) into a hierarchical optimization framework capable of solving a class of coordination problems in multi-vehicle networks. A critical issue in MPC/MILP applications is that the underlying optimization problem must be solved on-line. This introduces a time constraint that is hard to meet when the number of vehicles and the number of obstacles increase. To alleviate this problem, we implement some heuristics that significantly improve the efficiency of the proposed hierarchical, decentralized optimization scheme. Numerical simulations verify the scalability of the algorithm to the number of vehicles and complexity of the environment.