Task Planning with Mixed-Integer Programming for Multiple Cooking Task Using dual-arm Robot

Abstract

This work proposes a task scheduling method in an optimization framework with applications on a dual-arm cooking robot in a controlled cooking environment. A mixed-integer programming (MIP) framework is used to find an optimal sequence of tasks to be done for each arm. The optimization is fast and simple as a priori information about the tasks to be scheduled reveal dependency and kinematic constraints between them which significantly reduces the problem size as infeasible solutions are removed pre-optimization. The optimization approach's feasibility is validated on a series of simulations and an in-depth scalability analysis is conducted by changing the number of tasks to be done, the dishes to be completed, as well as the locations where the tasks can be done. Considering the unique configuration of the platform, analysis on selecting the minimum time required tasks as opposed tasks that will give the most flexibility to the other arm is also done. An example is presented on a real set of tasks to show the optimality of the solution.