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

2025-06-04

AI Summary

To address low efficiency and poor adaptability in collaborative perception scheduling of heterogeneous multi-agent systems (drones, personnel, and ground vehicles) within dynamic post-disaster environments, this paper proposes an online autonomous collaborative perception scheduling method. The approach introduces two key innovations: (1) an adaptive dimensionality-reduction matching mechanism that maps a five-dimensional state space to two distinct three-dimensional subspaces, and (2) a local Nash equilibrium game-theoretic decision framework based on Softmax for decentralized coordination. These designs jointly ensure scheduling stability while significantly improving real-time responsiveness. Experimental results demonstrate that the proposed method achieves average improvements of 64.12%, 46.48%, 16.55%, and 14.03% in task completion rate over GREEDY, K-WTA, MADL, and MARL baselines, respectively; moreover, each scheduling instance completes within under 10 seconds. The method thus enhances both timeliness and robustness of situational awareness acquisition in post-disaster scenarios.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Coordination and Collaboration
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Natural disasters have caused significant losses to human society, and the timely and efficient acquisition of post-disaster environmental information is crucial for the effective implementation of rescue operations. Due to the complexity of post-disaster environments, existing sensing technologies face challenges such as weak environmental adaptability, insufficient specialized sensing capabilities, and limited practicality of sensing solutions. This paper explores the heterogeneous multi-agent online autonomous collaborative scheduling algorithm HoAs-PALN, aimed at achieving efficient collection of post-disaster environmental information. HoAs-PALN is realized through adaptive dimensionality reduction in the matching process and local Nash equilibrium game, facilitating autonomous collaboration among time-dependent UAVs, workers and vehicles to enhance sensing scheduling. (1) In terms of adaptive dimensionality reduction during the matching process, HoAs-PALN significantly reduces scheduling decision time by transforming a five-dimensional matching process into two categories of three-dimensional matching processes; (2) Regarding the local Nash equilibrium game, HoAs-PALN combines the softmax function to optimize behavior selection probabilities and introduces a local Nash equilibrium determination mechanism to ensure scheduling decision performance. Finally, we conducted detailed experiments based on extensive real-world and simulated data. Compared with the baselines (GREEDY, K-WTA, MADL and MARL), HoAs-PALN improves task completion rates by 64.12%, 46.48%, 16.55%, and 14.03% on average, respectively, while each online scheduling decision takes less than 10 seconds, demonstrating its effectiveness in dynamic post-disaster environments.

Problem

• Efficient post-disaster environmental information collection
• Autonomous collaboration among UAVs, workers, and vehicles
• Reducing scheduling decision time and improving task completion

Innovation

• HoAs-PALN algorithm for multi-agent scheduling
• Adaptive dimensionality reduction in matching
• Local Nash equilibrium game optimization