POMDP模型在多机器人环境探测中的应用研究

孟磊; 吴芝亮; 王轶强

doi:10.13433/j.cnki.1003-8728.20200318

POMDP模型在多机器人环境探测中的应用研究

doi: 10.13433/j.cnki.1003-8728.20200318

天津大学机械工程学院, 天津 300354

基金项目:

国家自然科学基金项目 51205277

详细信息

作者简介:
孟磊(1996-), 硕士研究生, 研究方向为分布式多机器人非参数化信息感知算法研究, menglei1011@tju.edu.cn

通讯作者:
吴芝亮, 副教授, 研究生导师, zhlwu@tju.edu.cn

中图分类号: TP242
计量
- 文章访问数: 175
- HTML全文浏览量: 104
- PDF下载量: 18
- 被引次数: 0
出版历程
- 收稿日期: 2020-08-08
- 刊出日期: 2022-02-25

Research on Multi-robot Environment Exploration using POMDP

School of Mechanical Engineering, Tianjin University, Tianjin 300354, China

摘要

摘要: 为了提高多机器人环境探测的效率和精度，本文提供了一种基于部分可观马尔可夫决策过程(Partially observable markov decision process, POMDP)的路径规划方法来控制多个装有传感器的机器人实现对环境的协同探测。建立了多机器人环境探测系统的POMDP模型，以信息熵作为回报函数，令机器人沿着信息熵最大的方向不断移动。机器人对环境的信念采用非参数的、基于样本的表示，并用贝叶斯滤波来更新机器人对环境的信念。在仿真试验中，对两种环境的CO浓度进行了探测，都得到了精确的测量结果。与传统的全覆盖路径规划的方法相比，该方法在效率和精度上都具有优势。
- 多机器人 /
- 环境探测 /
- POMDP /
- 贝叶斯滤波 /
- 路径规划
Abstract: To improve the efficiency and accuracy for exploring a multi-robot environment, this paper proposes a path planning method based on the partially observable Markov decision process (POMDP) to control multiple robots equipped with sensors and to realize the coordinated exploration of the environment. Taken information entropy as the return function, the multi-robot environment exploration system based on the POMDP is established to move the robots with the largest information gain in the direction. The robot′s belief in the environment uses a non-parametric, sample-based representation, and the Bayesian filtering is used to update the robot′s belief in the environment. With our simulation software, the CO concentration of the two environments was precepted. The exploration results are in a good agreement with the predesigned environment. Compared with the traditional full coverage path planning method, the system proposed in this paper has advantages in both efficiency and accuracy.
- multi-robot /
- environment exploration /
- partial observable markov decision process (POMDP) /
- Bayesian filtering /
- path planning

HTML全文

图 1 机器人下一步探测点选择

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图 2 分布式控制体系结构多机器人系统

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图 3 多机器人环境探测流程图

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图 4 待探测90 m×120 m真实场

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图 5 多机器人探测路径(90 m×120 m)

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图 6 90 m×120 m测量场

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图 7 90 m×120 m误差场

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图 8 待探测60 m×70 m真实场

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图 9 多机器人探测路径(60 m×70 m)

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图 10 60 m×70 m误差场

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图 11 多机器人全覆盖式路径

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图 12 60 m×70 m全覆盖路径规划误差场

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图 13 60 m×70 m场均方根误差曲线

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