Assigning Multi-Robot Tasks to Multitasking Robots

TL;DR

This paper introduces a novel task allocation framework for multitasking robots, incorporating physical constraints, and demonstrates its effectiveness through simulations and real-world experiments, improving efficiency over traditional single-tasking methods.

Contribution

It presents a new framework for assigning multi-robot tasks to multitasking robots, considering physical constraints often ignored in prior work.

Findings

Outperforms single-tasking baselines in synthetic domains

Enhances task efficiency in site-clearing simulations

Shows practical benefits in real-world experiments

Abstract

One simplifying assumption in existing and well-performing task allocation methods is that the robots are single-tasking: each robot operates on a single task at any given time. While this assumption is harmless to make in some situations, it can be inefficient or even infeasible in others. In this paper, we consider assigning multi-robot tasks to multitasking robots. The key contribution is a novel task allocation framework that incorporates the consideration of physical constraints introduced by multitasking. This is in contrast to the existing work where such constraints are largely ignored. After formulating the problem, we propose a compilation to weighted MAX-SAT, which allows us to leverage existing solvers for a solution. A more efficient greedy heuristic is then introduced. For evaluation, we first compare our methods with a modern baseline that is efficient for single-tasking robots to validate the benefits of multitasking in synthetic domains. Then, using a site-clearing scenario in simulation, we further illustrate the complex task interaction considered by the multitasking robots in our approach to demonstrate its performance. Finally, we demonstrate a physical experiment to show how multitasking enabled by our approach can benefit task efficiency in a realistic setting.