Collaborative Route Planning of UAVs, Workers and Cars for Crowdsensing in Disaster Response

TL;DR

This paper introduces MANF-RL-RP, a novel multi-agent route planning algorithm for UAVs, workers, and cars to improve data collection efficiency in disaster response scenarios through advanced information processing and modeling techniques.

Contribution

The paper presents a new heterogeneous multi-agent route planning algorithm that integrates global-local information processing and a unified model structure for different agent types.

Findings

MANF-RL-RP outperforms baseline algorithms in task completion rate

Significant improvement over Greedy-SC-RP and MANF-DNN-RP

Effective in simulated disaster response environments

Abstract

Efficiently obtaining the up-to-date information in the disaster-stricken area is the key to successful disaster response. Unmanned aerial vehicles (UAVs), workers and cars can collaborate to accomplish sensing tasks, such as data collection, in disaster-stricken areas. In this paper, we explicitly address the route planning for a group of agents, including UAVs, workers, and cars, with the goal of maximizing the task completion rate. We propose MANF-RL-RP, a heterogeneous multi-agent route planning algorithm that incorporates several efficient designs, including global-local dual information processing and a tailored model structure for heterogeneous multi-agent systems. Global-local dual information processing encompasses the extraction and dissemination of spatial features from global information, as well as the partitioning and filtering of local information from individual agents. Regarding the construction of the model structure for heterogeneous multi-agent, we perform the following work. We design the same data structure to represent the states of different agents, prove the Markovian property of the decision-making process of agents to simplify the model structure, and also design a reasonable reward function to train the model. Finally, we conducted detailed experiments based on the rich simulation data. In comparison to the baseline algorithms, namely Greedy-SC-RP and MANF-DNN-RP, MANF-RL-RP has exhibited a significant improvement in terms of task completion rate.