A mechanism design model in robot-service-queue control with strategic operators and asymmetric information

Understanding various factors affecting human operator's performance is one key to effectively controlling a multi-robot service queue. In this paper we study the optimal incentive design and task allocation scheme in the presence of strategic human operators with unknown capability information. We build a mechanism design framework to model and analyze the problem. We show that a simple and easy-to-implement two-payment-level system can motivate operators to explore the appropriateness of their adopted workload and to devote their capacities optimal to the system. The subsequent analytical and numerical investigation provides insights into understanding the way it affects the control strategies in a multi-robot service queue to account for human operators' strategic behaviors.

I. INTRODUCTION

In recent decades the number of mobile robots deployed in field applications has risen dramatically. Their usage offers obvious advantages of reduced costs, removing humans from harm's way, or enabling entirely new applications that were previously impossible, especially when combining many such robots into a comprehensive system. The tasks and missions already carried out by large robot teams range from search, exploration, rescue, surveillance, pursuit, up to deploying infrastructure. The domains of application are equally diverse and range from low-cost warehouse security to interplanetary exploration. New developments in commodity hardware which serve as low cost replacements for otherwise expensive sensing or motion capabilities promise to further accelerate the trend towards deploying large teams of mobile robots.