A multi-agent semi-cooperative unmanned air traffic management model with separation assurance

被引:3
|
作者
Liu, Yanchao [1 ]
机构
[1] Wayne State Univ, Dept Ind & Syst Engn, Detroit, MI USA
基金
美国国家科学基金会;
关键词
Drone delivery; Nonlinear optimization; Air traffic management; OPTIMIZATION; RESOLUTION; ALGORITHM;
D O I
10.1016/j.ejtl.2021.100058
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
This paper presents an air traffic management framework to enable multiple fleets of unmanned aerial vehicles to traverse dense, omni-directional air traffic safely and efficiently. The main challenge addressed here is separation assurance in the absence of full coordination and communication. In this framework, each fleet is independently managed by a routing agent, which progressively plans the non-overlapping move-ahead corridors for vehicles in the fleet by solving a nonlinear optimization model. The model is artfully designed so that agents of different fleets need not engage in complicated multilateral communications or make guesses about external vehicles' flight intents to maintain effective inter-vehicle separation. For a complex routing problem, the framework is able to support centralized fleet routing, decentralized vehicle self-routing, and any other agent-vehicle configuration in between, allowing for customized trade-off between response time and traffic efficiency. Innovative algorithmic enhancements for solving the agent's nonconvex routing problem are prescribed with detailed annotation. The effectiveness and noteworthy properties of the framework are demonstrated by several simulation experiments.
引用
收藏
页数:14
相关论文
共 50 条
  • [31] RAILWAY TRAFFIC MANAGEMENT IMPLEMENTED BY A MULTI-AGENT SYSTEM
    Santa, Maria-Magdalena
    Cuibus, Octavian
    Letia, Tiberiu
    Enache, Mirela
    QUALITY AND INNOVATION IN ENGINEERING AND MANAGEMENT, 2011, : 493 - 498
  • [32] Multi-Agent Traffic Management System for Emergency Vehicle
    Imane, Chakir
    El Khaili, Mohamed
    Mestari, Mohamed
    2022 2ND INTERNATIONAL CONFERENCE ON INNOVATIVE RESEARCH IN APPLIED SCIENCE, ENGINEERING AND TECHNOLOGY (IRASET'2022), 2022, : 308 - 315
  • [33] The SCAUP model: Multi-agent simulation from Urban sensors for traffic air pollution
    Emery, Justin
    Marilleau, Nicolas
    Martiny, Nadege
    Thevenin, Thomas
    CYBERGEO-EUROPEAN JOURNAL OF GEOGRAPHY, 2020,
  • [34] Multi-agent cooperative control for traffic signal on geographic road network
    Zheng Y.
    Guo R.
    Ma D.
    Zhao Z.
    Li X.
    Cehui Xuebao/Acta Geodaetica et Cartographica Sinica, 2021, 50 (09): : 1203 - 1210
  • [35] Multi-agent situation awareness error evolution in air traffic
    Blom, HAP
    Stroeve, SH
    PROBABILISTIC SAFETY ASSESSMENT AND MANAGEMENT, VOL 1- 6, 2004, : 272 - 277
  • [36] Cooperative Multi-Agent Traffic Monitoring Can Reduce Camera Surveillance
    Guastella, Davide Andrea
    Pournaras, Evangelos
    IEEE ACCESS, 2023, 11 : 142125 - 142145
  • [37] Multi-Agent Reinforcement Learning for Traffic Signal Control: A Cooperative Approach
    Kolat, Mate
    Kovari, Balint
    Becsi, Tamas
    Aradi, Szilard
    SUSTAINABILITY, 2023, 15 (04)
  • [38] Cooperative Traffic Signal Control Based on Multi-agent Reinforcement Learning
    Gao, Ruowen
    Liu, Zhihan
    Li, Jinglin
    Yuan, Quan
    BLOCKCHAIN AND TRUSTWORTHY SYSTEMS, BLOCKSYS 2019, 2020, 1156 : 787 - 793
  • [39] Microscopic Traffic Simulation by Cooperative Multi-agent Deep Reinforcement Learning
    Bacchiani, Giulio
    Molinari, Daniele
    Patander, Marco
    AAMAS '19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS, 2019, : 1547 - 1555
  • [40] A multi-agent model for decision oriented cooperative systems
    ElMansouri, H
    Alquier, AM
    Zarate, P
    COOP '96 - SECOND INTERNATIONAL WORKSHOP ON THE DESIGN OF COOPERATIVE SYSTEMS, 1996, : 357 - 373