Mixed-integer programming in motion planning

This paper presents a review of past and present results and approaches in the area of motion planning using MIP (Mixed-integer Programming). Although in the early 2000s MIP was still seen with reluctance as method for solving motion planning-related problems, nowadays, due to increases in computational power and theoretical advances, its extensive modeling capabilities and versatility are coming to the fore and enjoy increased application and appreciation. This class of control problems involves, essentially, either a selection from a limited number of alternatives or a constrained optimization problem over a non-convex domain. In both situations, MIP has proven to be an efficient modeling technique as it will be shown in the present review paper. Furthermore, an emphasis is laid on the existing alternatives for implementation and on various experimental validations documented in the literature.

Automated summary

This paper reviews the past and current results and approaches in motion planning using Mixed-integer Programming (MIP). It highlights the efficiency of MIP in selecting from a limited number of alternatives or solving optimization problems over non-convex domains, as well as the importance of various experimental validations in the literature.