Collaborative Scheduling of Time-dependent UAVs,Vehicles and Workers for Crowdsensing in Disaster Response

2025-10-29

AI Summary

Post-disaster complex environments severely limit the adaptability, domain specificity, and practicality of existing sensing paradigms—e.g., mobile crowdsensing. To address this, we propose a heterogeneous multi-agent online collaborative scheduling framework enabling dynamic, real-time coordination among unmanned aerial vehicles (UAVs), ground vehicles, and human operators for joint environmental perception. Our approach innovatively models agent collaboration and conflict relationships as a weighted undirected graph and integrates a multi-priority-queue-driven iterative local search algorithm to support time-critical scheduling decisions. Experimental results demonstrate that, compared to baseline methods, our framework improves task completion rates by 12.89%–54.13% while ensuring each online scheduling decision completes within ≤3 seconds. This significantly enhances both timeliness and robustness of situational awareness acquisition in post-disaster scenarios.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Multiagent Planning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Frequent natural disasters cause significant losses to human society, and timely, efficient collection of post-disaster environmental information is the foundation for effective rescue operations. Due to the extreme complexity of post-disaster environments, existing sensing technologies such as mobile crowdsensing suffer from weak environmental adaptability, insufficient professional sensing capabilities, and poor practicality of sensing solutions. Therefore, this paper explores a heterogeneous multi-agent online collaborative scheduling algorithm, HoCs-MPQ, to achieve efficient collection of post-disaster environmental information. HoCs-MPQ models collaboration and conflict relationships among multiple elements through weighted undirected graph construction, and iteratively solves the maximum weight independent set based on multi-priority queues, ultimately achieving collaborative sensing scheduling of time-dependent UA Vs, vehicles, and workers. Specifically, (1) HoCs-MPQ constructs weighted undirected graph nodes based on collaborative relationships among multiple elements and quantifies their weights, then models the weighted undirected graph based on conflict relationships between nodes; (2) HoCs-MPQ solves the maximum weight independent set based on iterated local search, and accelerates the solution process using multi-priority queues. Finally, we conducted detailed experiments based on extensive real-world and simulated data. The experiments show that, compared to baseline methods (e.g., HoCs-GREEDY, HoCs-K-WTA, HoCs-MADL, and HoCs-MARL), HoCs-MPQ improves task completion rates by an average of 54.13%, 23.82%, 14.12%, and 12.89% respectively, with computation time for single online autonomous scheduling decisions not exceeding 3 seconds.

Problem

• Scheduling heterogeneous agents for disaster response crowdsensing
• Modeling collaboration conflicts via weighted graph construction
• Optimizing task completion through multi-priority queue algorithms

Innovation

• Models collaboration via weighted undirected graph construction
• Solves maximum weight independent set iteratively
• Accelerates solution using multi-priority queues