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

2025-08-27

AI Summary

Existing multi-robot task allocation (MRTA) methods in human–robot shared environments suffer from low efficiency due to their reliance on static maps and failure to model dynamic human motion. Method: This paper proposes a dynamic task allocation framework integrating human motion modeling. Its core innovation is the first incorporation of queryable spatiotemporal dynamic maps—termed Maps of Dynamics (MoDs)—into MRTA. MoDs are constructed from historical pedestrian trajectories to formulate a stochastic cost function that explicitly models human interference and enables proactive path anticipation. The framework couples an enhanced MRTA algorithm with stochastic optimization to jointly optimize robot paths and temporal task windows. Results: Experiments show that the proposed method reduces task completion time by up to 26% compared to static approaches ignoring dynamics, and improves upon baseline methods by 19%, significantly enhancing responsiveness and execution efficiency of multi-robot systems in dynamic human–robot coexistence scenarios.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: The sharing economy

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.

Problem

• Optimizing task allocation in multi-robot systems with human presence
• Addressing delays from human movement in shared robot environments
• Improving efficiency by incorporating human motion patterns in planning

Innovation

• Uses Maps of Dynamics for human movement patterns
• Integrates stochastic cost function with MoDs
• Reduces mission completion time significantly