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

Researchers propose UrbanHuRo, a two-layer human-robot collaboration framework that jointly optimizes different urban services like delivery and sensing. The system demonstrated 29.7% improvement in sensing coverage and 39.2% increase in courier income while reducing overdue orders through coordinated optimization of heterogeneous services.

• →UrbanHuRo introduces a novel framework for coordinating multiple urban services rather than optimizing them in isolation.
• →The system uses MapReduce-based K-submodular maximization for efficient order dispatch and deep reinforcement learning for sensing route planning.
• →Real-world testing showed 29.7% better sensing coverage and 39.2% higher courier income compared to traditional approaches.
• →The framework enables human couriers to collect environmental data while robots assist with deliveries during peak hours.
• →Joint optimization of urban services addresses conflicting objectives and real-time coordination challenges in dynamic environments.