.png){kind=icon}
返回
Robotic Exploration for Learning Human Motion Patterns
.png){kind=icon}
PRE
AI
DOI：10.1109/TRO.2021.3101358.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Understanding how people are likely to move is key to efficient and safe robot navigation in human environments. However, mobile robots can only observe a fraction of the environment at a time, while the activity patterns of people may also change at different times. This article introduces a new methodology for mobile robot exploration to maximize the knowledge of human activity patterns by deciding where and when to collect observations. We introduce an exploration policy driven by the entropy levels in a spatio-temporal map of pedestrian flows, and compare multiple spatio-temporal exploration strategies including both informed and uninformed approaches. The evaluation is performed by simulating mobile robot exploration using real sensory data from three long-term pedestrian datasets. The results show that for certain scenarios the models built with proposed exploration system can better predict the flow patterns than uninformed strategies, allowing the robot to move in a more socially compliant way, and that the exploration ratio is a key factor when it comes to the model prediction accuracy.
Keyword:
Robots
Predictive models
Uncertainty
Robot sensing systems
Buildings
Navigation
Data models
Human motion model
long-term data
mobile robots
spatio-temporal exploration
期刊
IF:
10.5
论文数:
3.3K
被引数:
2.8W
机构
引用论文
Motor Vehicle CO2 Emissions in the United States: Potential Behavioral Feedback and Global Warming
OAAI
.png){kind=icon}
OAAI分享收藏分享收藏分享收藏分享收藏
查看更多内容