| 引用本文: | 华承昊,窦丽华,方浩.多机器人最大熵博弈协同定位算法.[J].国防科技大学学报,2014,36(2):192-198.[点击复制] |
| HUA Chenghao,DOU Lihua,FANG Hao.A new cooperative localization algorithm based on maximum entropy gaming[J].Journal of National University of Defense Technology,2014,36(2):192-198[点击复制] |
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| 多机器人最大熵博弈协同定位算法 |
| 华承昊1,2, 窦丽华1,2, 方浩1,2 |
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(1.北京理工大学 自动化学院，北京 100081;2.北京理工大学 复杂系统的智能控制与决策重点实验室，北京 100081)
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| 摘要: |
| 研究了多机器人观测到同一目标时的协同定位问题。建立了各个机器人相对观测一致程度的数学描述模型，进而提出用基于极大熵准则的最大熵博弈获取使相对观测一致程度最优的协同定位方式。针对博弈结果的多样性，相应地改变观测方程的雅克比矩阵，推导了可适应多机器人各种博弈结果的扩展Kalman滤波协同定位算法。仿真实验表明，方法可实现机器人团队在协同定位时有选择、更高效地共享相互间的观测信息；在保证协同定位精度提高的同时有效地消除了多机器人相对观测信息间的冲突。 |
| 关键词: 多机器人 最大熵博弈 一致相对观测 协同定位 扩展Kalman滤波算法 |
| DOI：10.11887/j.cn.201402032 |
| 投稿日期：2013-11-08 |
| 基金项目:北京市教育委员会共建项目专项资助（XK100070532） |
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| A new cooperative localization algorithm based on maximum entropy gaming |
| HUA Chenghao1,2, DOU Lihua1,2, FANG Hao1,2 |
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(1.School of Automation, Beijing Institute of Technology, Beijing 100081, China;2.Key Laboratory of Intelligent Control and Decision of Complex System, Beijing Institute of Technology, Beijing 100081, China)
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| Abstract: |
| The problem of cooperative localization in the situation when an object is detected by robots simultaneously was studied. As each robot has its own relative observation about the object, a mathematical model for comparing the consistency of these relative observations was presented. With that method, a new cooperative localization algorithm based on maximum entropy gaming and Extended Kalman Filter(EKF) was proposed. As the gaming results are different, the EKF equations that can match any gaming result were derived. Several simulation results showing that the proposed algorithm can improve the localization performance and avoid the relative observations conflict problem in cooperative localization in the meantime. |
| Keywords: multi-robot maximum entropy gaming consistent relative observations cooperative localization EKF algorithm |
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