Efficient Human-Aware Task Allocation for Multi-Robot Systems in Shared Environments

TL;DR

This paper presents a novel MRTA method that incorporates human movement patterns using Maps of Dynamics to improve task allocation efficiency in shared environments, reducing mission times significantly.

Contribution

Introduces a human-aware MRTA approach leveraging MoDs to account for human motion, enhancing task allocation efficiency in environments with people.

Findings

Reduced mission completion times by up to 26% with MoDs.

Demonstrated effectiveness of human dynamics in MRTA.

Outperformed dynamics-agnostic and baseline methods.

Abstract

Multi-robot systems are increasingly deployed in applications, such as intralogistics or autonomous delivery, where multiple robots collaborate to complete tasks efficiently. One of the key factors enabling their efficient cooperation is Multi-Robot Task Allocation (MRTA). Algorithms solving this problem optimize task distribution among robots to minimize the overall execution time. In shared environments, apart from the relative distance between the robots and the tasks, the execution time is also significantly impacted by the delay caused by navigating around moving people. However, most existing MRTA approaches are dynamics-agnostic, relying on static maps and neglecting human motion patterns, leading to inefficiencies and delays. In this paper, we introduce \acrfull{method name}. This method leverages Maps of Dynamics (MoDs), spatio-temporal queryable models designed to capture historical human movement patterns, to estimate the impact of humans on the task execution time during deployment. \acrshort{method name} utilizes a stochastic cost function that includes MoDs. Experimental results show that integrating MoDs enhances task allocation performance, resulting in reduced mission completion times by up to $26\%$ compared to the dynamics-agnostic method and up to $19\%$ compared to the baseline. This work underscores the importance of considering human dynamics in MRTA within shared environments and presents an efficient framework for deploying multi-robot systems in environments populated by humans.