Trust in Multi-Vehicle Systems Using MDP Control Strategies

被引:0
|
作者
Delamer, Jean-Alexis [1 ]
Givigi, Sidney [1 ]
机构
[1] Queens Univ Kingston, Sch Comp, 557 Goodwin Hall, Kingston, ON, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
D O I
10.1109/smc42975.2020.9283302
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
This paper proposes a protocol that ensures trust between two vehicles in a multi-vehicle system. Trust is the implicit assessment that another vehicle will follow a predetermined strategy. The communication is done through a channel and the quantity of information transferred is guaranteed to be small. For privacy, the channel can be encrypted, but the message can only be decoded if the vehicles know the control strategy being followed. The protocol is implemented for a problem of two Unmanned Aerial Vehicles (UAVs) trying to find a target in a maze. The control strategy is implemented using Markov Decision Processes (MDPs). Simulations of the protocol demonstrate that communication is received and decoded by the teammates without explicitly revealing the tactics being used.
引用
收藏
页码:2377 / 2382
页数:6
相关论文
共 50 条
  • [31] Multi-vehicle Control and Optimization for Spatiotemporal Sampling
    Sydney, Nitin
    Paley, Derek A.
    2011 50TH IEEE CONFERENCE ON DECISION AND CONTROL AND EUROPEAN CONTROL CONFERENCE (CDC-ECC), 2011, : 5607 - 5612
  • [32] Multi-vehicle cooperative control flight test
    Pongpunwattana, Anawat
    Wise, Richard
    Rysdyk, Rolf
    Kang, Anthony J.
    2006 IEEE/AIAA 25TH DIGITAL AVIONICS SYSTEMS CONFERENCE, VOLS 1- 3, 2006, : 781 - 791
  • [33] Coordination and control experiments on a multi-vehicle testbed
    King, E
    Kuwata, Y
    Alighanbari, M
    Bertuccelli, L
    How, J
    PROCEEDINGS OF THE 2004 AMERICAN CONTROL CONFERENCE, VOLS 1-6, 2004, : 5315 - 5320
  • [34] A decomposition approach to multi-vehicle cooperative control
    Earl, Matthew G.
    D'Andrea, Raffaello
    ROBOTICS AND AUTONOMOUS SYSTEMS, 2007, 55 (04) : 276 - 291
  • [35] Cooperative Collision Avoidance for Multi-Vehicle Systems Using Reinforcement Learning
    Wang, Qichen
    Phillips, Chris
    2013 18TH INTERNATIONAL CONFERENCE ON METHODS AND MODELS IN AUTOMATION AND ROBOTICS (MMAR), 2013, : 98 - 102
  • [36] Autonomous Multi-Vehicle Formations Using A Pseuodospectral Optimal Control Framework
    Hurni, Michael A.
    Sekhavat, Pooya
    Karpenko, Mark
    Ross, I. Michael
    2010 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM), 2010,
  • [37] Multi-vehicle formation control in uncertain environments
    Franze, Giuseppe
    Lucia, Walter
    Famularo, Domenico
    2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2017,
  • [38] An Underwater Robotic Testbed for Multi-Vehicle Control
    Kitts, Christopher
    Adamek, Thomas
    Vlahos, Michael
    Mahacek, Anne
    Poore, Killian
    Guerra, Jorge
    Neumann, Michael
    Chin, Matthew
    Rasay, Mike
    2014 IEEE/OES AUTONOMOUS UNDERWATER VEHICLES (AUV), 2014,
  • [39] Multi-Vehicle Tracking Adaptive Cruise Control
    Ki, Moon Il
    Kyongsu, Yi
    TRANSACTIONS OF THE KOREAN SOCIETY OF MECHANICAL ENGINEERS A, 2005, 29 (01) : 139 - 144
  • [40] Multi-vehicle Cooperative Control for Load Transportation
    Valentim, Tiago
    Cunha, Rita
    Oliveira, Paulo
    Cabecinhas, David
    Silvestre, Carlos
    IFAC PAPERSONLINE, 2019, 52 (12): : 358 - 363