Towards Safe and Efficient Swarm-Human Collaboration: A Hierarchical Multi-Agent Pickup and Delivery framework

TL;DR

This paper introduces a hierarchical multi-agent framework for safe and efficient collaboration between humans and robots in pickup and delivery tasks within intelligent storage systems, addressing unpredictability and dynamic environments.

Contribution

It proposes a novel two-layer decision framework for human-swarm hybrid systems, integrating mode assignment, task allocation, and path planning with mixed-integer programming.

Findings

Framework effectively handles task variability and human unpredictability.

Simulation demonstrates improved coordination and efficiency.

Algorithm shows practical viability in complex scenarios.

Abstract

The multi-Agent Pickup and Delivery (MAPD) problem is crucial in the realm of Intelligent Storage Systems (ISSs), where multiple robots are assigned with time-varying, heterogeneous, and potentially uncertain tasks. When it comes to Human-Swarm Hybrid System ((HS)$_2$), robots and human workers will accomplish the MAPD tasks in collaboration. Herein, we propose a Human-Swarm Hybrid System Pickup and Delivery ((HS)$_2$PD) framework, which is predominant in future ISSs. A two-layer decision framework based on the prediction horizon window is established in light of the unpredictability of human behavior and the dynamic changes of tasks. The first layer is a two-level programming problem to solve the problems of mode assignment and TA. The second layer is devoted to the exact path of each agent via solving mixed-integer programming (MIP) problems. An integrated algorithm for the (HS)$_2$PD problem is summarized. The practicality and validity of the above algorithm are illustrated via a numerical simulation example towards (HS)$_2$PD tasks.