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UrbanHuRo: A Two-Layer Human-Robot Collaboration Framework for the Joint Optimization of Heterogeneous Urban Services

Tonmoy Dey, Lin Jiang, Zheng Dong, Guang Wang

TL;DR

UrbanHuRo is proposed, a two-layer human-robot collaboration framework for joint optimization of heterogeneous urban services, demonstrated through crowdsourced delivery and urban sensing, which includes a scalable distributed MapReduce-based K-submodular maximization module for efficient order dispatch and a deep submodular reward reinforcement learning algorithm for sensing route planning.

Abstract

In the vision of smart cities, technologies are being developed to enhance the efficiency of urban services and improve residents' quality of life. However, most existing research focuses on optimizing individual services in isolation, without adequately considering reciprocal interactions among heterogeneous urban services that could yield higher efficiency and improved resource utilization. For example, human couriers could collect traffic and air quality data along their delivery routes, while sensing robots could assist with on-demand delivery during peak hours, enhancing both sensing coverage and delivery efficiency. However, the joint optimization of different urban services is challenging due to potentially conflicting objectives and the need for real-time coordination in dynamic environments. In this paper, we propose UrbanHuRo, a two-layer human-robot collaboration framework for joint optimization of heterogeneous urban services, demonstrated through crowdsourced delivery and urban sensing. UrbanHuRo includes two key designs: (i) a scalable distributed MapReduce-based K-submodular maximization module for efficient order dispatch, and (ii) a deep submodular reward reinforcement learning algorithm for sensing route planning. Experimental evaluations on real-world datasets from a food delivery platform demonstrate that UrbanHuRo improves sensing coverage by 29.7% and courier income by 39.2% on average in most settings, while also significantly reducing the number of overdue orders.

UrbanHuRo: A Two-Layer Human-Robot Collaboration Framework for the Joint Optimization of Heterogeneous Urban Services

TL;DR

UrbanHuRo is proposed, a two-layer human-robot collaboration framework for joint optimization of heterogeneous urban services, demonstrated through crowdsourced delivery and urban sensing, which includes a scalable distributed MapReduce-based K-submodular maximization module for efficient order dispatch and a deep submodular reward reinforcement learning algorithm for sensing route planning.

Abstract

In the vision of smart cities, technologies are being developed to enhance the efficiency of urban services and improve residents' quality of life. However, most existing research focuses on optimizing individual services in isolation, without adequately considering reciprocal interactions among heterogeneous urban services that could yield higher efficiency and improved resource utilization. For example, human couriers could collect traffic and air quality data along their delivery routes, while sensing robots could assist with on-demand delivery during peak hours, enhancing both sensing coverage and delivery efficiency. However, the joint optimization of different urban services is challenging due to potentially conflicting objectives and the need for real-time coordination in dynamic environments. In this paper, we propose UrbanHuRo, a two-layer human-robot collaboration framework for joint optimization of heterogeneous urban services, demonstrated through crowdsourced delivery and urban sensing. UrbanHuRo includes two key designs: (i) a scalable distributed MapReduce-based K-submodular maximization module for efficient order dispatch, and (ii) a deep submodular reward reinforcement learning algorithm for sensing route planning. Experimental evaluations on real-world datasets from a food delivery platform demonstrate that UrbanHuRo improves sensing coverage by 29.7% and courier income by 39.2% on average in most settings, while also significantly reducing the number of overdue orders.
Paper Structure (23 sections, 11 equations, 5 figures, 1 table, 2 algorithms)

This paper contains 23 sections, 11 equations, 5 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Design of UrbanHuRo
  4. Environment and Simulator
  5. Efficient Two-layer Decision-making
  6. Optimization Objectives
  7. KSubMR for Order Dispatch Decision-making
  8. DSRQN for Urban Sensing Decision-making
  9. Evaluation
  10. Evaluation Setup
  11. Parameter Setting and Experiment Environment
  12. Evaluation Metrics
  13. Datasets
  14. Baselines
  15. Evaluation Results
  16. ...and 8 more sections

Figures (5)

  • Figure 1: Intuition of Human-Robot Collaboration
  • Figure 3: An Overview of the Proposed KSubMR
  • Figure 4: Overdue orders across three key time periods, i.e., morning rush hours, noon peak hours, and evening rush hours.
  • Figure 5: Average hourly income of couriers under different numbers of RVs.
  • Figure 6: Ablation Studies