Dempster-Shafer theory for sensor fusion in autonomous mobile robots
Dempster-Shafer theory for sensor fusion in autonomous mobile robots
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DOI：
10.1109/70.681240
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发表时间：
1998-04-01
期刊：
影响因子：
--
通讯作者：
Murphy, RR
中科院分区：
文献类型：
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作者：
Murphy, RR
本文讨论了Dempster-Shafer （DS）理论在自主移动机器人传感器融合中的应用，它利用了DS理论中两个很少使用的组件：冲突度量的权重和识别框架的扩大，冲突权重用于衡量不同传感器之间的共识量，缺乏共识会导致机器人在一定范围内进行补偿或进一步调查问题，从而增加机器人运行的鲁棒性。扩大识别框架允许对证据进行模块化分解，这种分解提供了感知抽象的优势，并允许将有关该领域的专家知识嵌入到识别框架中，简化了知识库的构建和维护。利用该框架进行了六次实验，并利用移动机器人收集了四种类型的传感器数据，并将其与传感器融合效果（SFX）架构进行了融合。
This article discusses Dempster-Shafer (DS) theory in terms of its utility for sensor fusion for autonomous mobile robots, It exploits two little used components of DS theory: the weight of conflict metric and the enlargement of the frame of discernment, The weight of conflict is used to measure the amount of consensus between different sensors, A lack of consensus leads the robot to either compensate within certain limits or investigate the problem further, adding robustness to the robot's operation. Enlarging the frame of discernment allows a modular decomposition of evidence, This decomposition offers the advantages of perceptual abstraction, and permits expert knowledge about the domain to be embedded in the frames of discernment, simplifying the construction and maintenance of the knowledge base. Six experiments using this Dempster-Shafer framework are presented, Data from four types of sensor data were collected by a mobile robot and fused with the Sensor Fusion Effects (SFX) architecture.