UAV-delivery-management-system

A Real-Time System for Scheduling and Managing UAV Delivery in Urban

paper: http://arxiv.org/abs/2412.11590

💫 Introduction

Abstract

As urban logistics demand continues to grow, UAV delivery has become a key solution to improve delivery efficiency, reduce traffic congestion, and lower logistics costs. However, to fully leverage the potential of UAV delivery networks, efficient swarm scheduling and management are crucial. In this paper, we propose a real-time scheduling and management system based on the ``Airport-Unloading Station" model, aiming to bridge the gap between high-level scheduling algorithms and low-level execution systems. This system, acting as middleware, accurately translates the requirements from the scheduling layer into specific execution instructions, ensuring that the scheduling algorithms perform effectively in real-world environments. Additionally, we implement three collaborative scheduling schemes involving autonomous ground vehicles (AGVs), unmanned aerial vehicles (UAVs), and ground staff to further optimize overall delivery efficiency. Through extensive experiments, this study demonstrates the rationality and feasibility of the proposed management system, providing practical solution for the commercial application of UAVs delivery in urban.

System overview

📷 Experiments

🎵 Quick start

1. Pull the image through docker (race_images.sh)

chmod +x race_images.sh
./race_images.sh

docker network create --subnet=192.168.100.0/24 race_net
docker network inspect race_net

2. Start the docker file (start_race.sh)

chmod +x stop_race.sh
./start_race.sh

3. Put the race_demo into the container of the race_user_sdk_container/home/, then you can use ros to compile and run.

4. Stop the docker file (stop_race.sh)

chmod +x start_race.sh
./stop_race.sh

Core code

In the ./race_demo/src, there are our code code.

msg

In the ./race_demo/msg, it shows the type of message published on the topic

Configuration Files

In the configuration folder you can see the car drone and scene parameter settings as well as the order details.

car config

drone config

scene config

order config

order four time attributes

{
    "betterTime": 1500,
    "cargoParam": {
        "birthplace": {
            "x": 190,
            "y": 425,
            "z": -16
        },
        "index": 47,
        "name": "测试货物47",
        "weight": 3.947
    },
    "index": 47,
    "loadingStationInfo": {
        "$ref": "$.taskParam.waybillParamList[46].cargoParam.birthplace"
    },
    "orderTime": 300,
    "targetPosition": {
        "x": 490,
        "y": 390,
        "z": -22
    },
    "timeout": 1800
},