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

arXiv: https://arxiv.org/abs/2506.04276 · HTML: https://mlsanity.com/paper/2506.04276/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 58 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction](https://mlsanity.com/paper/2402.11838/) (2024)
- [Collaboration Helps Camera Overtake LiDAR in 3D Detection](https://mlsanity.com/paper/2303.13560/) (2023)
- [UrbanGPT: Spatio-Temporal Large Language Models](https://mlsanity.com/paper/2403.00813/) (2024)
- [CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban Driving](https://mlsanity.com/paper/2202.08557/) (2022)
- [Stronger Together: Air-Ground Robotic Collaboration Using Semantics](https://mlsanity.com/paper/2206.14289/) (2022)
- [Cooperative Aerial-Ground Multi-Robot System for Automated Construction Tasks](https://mlsanity.com/paper/2204.13926/) (2022)
- [Radiation Search Operations using Scene Understanding with Autonomous UAV and UGV](https://mlsanity.com/paper/1609.00017/) (2016)
- [Decentralized Dynamic Task Allocation in Swarm Robotic Systems for Disaster Response](https://mlsanity.com/paper/1907.04394/) (2019)
- [Mobile Wireless Rechargeable UAV Networks: Challenges and Solutions](https://mlsanity.com/paper/2203.13139/) (2022)
- [Collaborative Route Planning of UAVs, Workers and Cars for Crowdsensing in Disaster Response](https://mlsanity.com/paper/2308.11088/) (2023)
- [Object Tracking by Least Spatiotemporal Searches](https://mlsanity.com/paper/2007.09288/) (2020)

## Abstract (arXiv)

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.