Redundant Robot Assignment on Graphs with Uncertain Edge Costs
This addresses the challenge of efficient robot assignment in transport networks with uncertain edge costs, though it is incremental as it builds on existing assignment frameworks.
The paper tackles the problem of assigning multiple robots to goal locations under uncertain travel times by using redundant robots to minimize average waiting times, achieving reductions in waiting times through a polynomial-time solution with sub-optimality bounds.
We provide a framework for the assignment of multiple robots to goal locations, when robot travel times are uncertain. Our premise is that time is the most valuable asset in the system. Hence, we make use of redundant robots to counter the effect of uncertainty and minimize the average waiting time at destinations. We apply our framework to transport networks represented as graphs, and consider uncertainty in the edge costs (i.e., travel time). Since solving the redundant assignment problem is strongly NP-hard, we exploit structural properties of our problem to propose a polynomial-time solution with provable sub-optimality bounds. Our method uses distributive aggregate functions, which allow us to efficiently (i.e., incrementally) compute the effective cost of assigning redundant robots. Experimental results on random graphs show that the deployment of redundant robots through our method reduces waiting times at goal locations, when edge traversals are uncertain.