THESIS
2020
1 online resource (xvi, 110 pages) : illustrations (chiefly color)
Abstract
The ability to navigate in dynamic environments has become an urgent demand for
robots working around humans. Challenges persist in robot navigation in dense crowds
or other dynamic environments. Whether it is achieved through a modular or end-to-end
pipeline, navigation in such environments suffers from the uncertainty of dynamic objects
and the variability of complex contexts. This thesis focuses on these factors to realize
efficient and effective navigation systems.
Navigation systems in low dynamic environments are first investigated. Conventional
navigation systems that follow the sense-plan-control pipeline tackle dynamic obstacles
with time-inefficient replanning or computationally demanding spatial-temporal optimization.
This thesis proposes a hierarchical trajectory planning me...[
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The ability to navigate in dynamic environments has become an urgent demand for
robots working around humans. Challenges persist in robot navigation in dense crowds
or other dynamic environments. Whether it is achieved through a modular or end-to-end
pipeline, navigation in such environments suffers from the uncertainty of dynamic objects
and the variability of complex contexts. This thesis focuses on these factors to realize
efficient and effective navigation systems.
Navigation systems in low dynamic environments are first investigated. Conventional
navigation systems that follow the sense-plan-control pipeline tackle dynamic obstacles
with time-inefficient replanning or computationally demanding spatial-temporal optimization.
This thesis proposes a hierarchical trajectory planning method that decouples the
spatial planning and temporal planning to reduce computation time and memory usage.
End-to-end visual navigation suffers from performance degradation caused by domain
shift or dataset bias. This thesis proposes to use human attention to enhance the generalization
capability of the end-to-end autonomous driving network. By selectively filtering
out task-irrelevant information, like the changeful background, the method shows significantly
better performance and lower model uncertainty in unseen environments than the
baseline.
In high dynamic environments, interaction modeling becomes essential for navigation
tasks. Graph representation is utilized to model the interactions among robots and
humans effectively. In collision avoidance, the crowd feature is aggregated through the
graph convolutional network (GCN), where the robot assigns different attention to the
neighborhood through the adjacency matrix. In pedestrian trajectory prediction, coherent
motion in the crowd is fully leveraged for interaction modeling. The intergroup and
intragroup interactions are captured separately through two graphs to give accurate and
realistic predictions. The experimental results prove the superiority of the methods over
the state-of-the-art methods in terms of navigation success rate and trajectory prediction
error. These modules can be further deployed to facilitate navigation in high dynamic
environments.
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