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Robust mobile robot navigation is an open problem.
I've done work in tracking moving objects, and mobile robot navigation.
Indeed, we are interested in mobile robot navigation using omnidirectional vision in structured environments.
This paper proposes online and adaptive elliptic trajectories to perform smooth and safe mobile robot navigation.
It provides us a scalable mobile robot navigation and obstacle avoidance, with less processing.
This paper proposes a new approach to mobile robot navigation using 3D vision and artificial landmarks.
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For mobile robots, navigation skill is one of their fundamental capabilities.
A combined short-term learning (STL) and long-term learning (LTL) approach to solving mobile-robot navigation problems is presented and tested in both the real and virtual domains.
Generally, D* is very effective for replanning in the context of mobile-robot navigation.
Path-planning algorithms can be the solution for many motion-planning issues, such as UAV path planning [1], in mobile robot outdoor navigation [19], in mobile robot indoor navigation [9] and even in video games [17].
Mobile robot's navigation in an unknown environment is used as a test bed for the proposed scaling strategies.
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