Resilient Assignment Using Redundant Robots on Transport Networks with Uncertain Travel Time

TL;DR

This paper introduces a polynomial-time near-optimal method for assigning redundant robots to transport network goals, minimizing average waiting times despite uncertain travel times, by leveraging supermodular optimization and greedy algorithms.

Contribution

It presents a novel, computationally efficient approach for resilient robot assignment under uncertainty, utilizing supermodular minimization with matroid constraints and providing theoretical bounds.

Findings

The proposed method outperforms benchmark algorithms in simulations.

Redundant robot assignments significantly reduce waiting times.

Diversity in robot teams enhances resilience to travel time uncertainty.

Abstract

This paper considers the problem of assigning multiple mobile robots to goals on transport networks with uncertain information about travel times. Our aim is to produce optimal assignments, such that the average waiting time at destinations is minimized. Since noisy travel time estimates result in sub-optimal assignments, we propose a method that offers resilience to uncertainty by making use of redundant robots. However, solving the redundant assignment problem optimally is strongly NP-hard. Hence, we exploit structural properties of our mathematical problem formulation to propose a polynomial-time, near-optimal solution. We demonstrate that our problem can be reduced to minimizing a supermodular cost function subject to a matroid constraint. This allows us to develop a greedy algorithm, for which we derive sub-optimality bounds. We demonstrate the effectiveness of our approach with simulations on transport networks, where uncertain edge costs and uncertain node positions lead to noisy travel time estimates. Comparisons to benchmark algorithms show that our method performs near-optimally and significantly better than non-redundant assignment. Finally, our findings include results on the benefit of diversity and complementarity in redundant robot coalitions; these insights contribute towards providing resilience to uncertainty through targeted robot team compositions.