Mixed-Integer and Constraint Programming Techniques for Mobile Robot Task Planning

Abstract

We investigate the use of optimization-based techniques to model and solve two real-world single robot task planning problems. In the first problem, a robot must plan a set of tasks, each with different temporal constraints. In the second problem, a socially interacting robot must plan a set of tasks while considering the schedules of multiple human users, as well as physical constraints including battery level. We apply existing mixed-integer programming and constraint programming techniques, yielding two exact methods for each problem. Numerical experiments show that both approaches produce better solutions in less time compared to techniques previously proposed for these problems. In order to confirm their physical utility, we implement the plans for the second problem in both simulated and real environments on Tangy, a socially interacting robot. We conclude that both mixed-integer programming and constraint programming are promising general approaches to robot task planning that should be considered when solving these problems.