Learning to Predict Ego-Vehicle Poses for Sampling-Based Nonholonomic Motion Planning
نویسندگان
چکیده
منابع مشابه
Learning Sampling Distributions for Robot Motion Planning
A defining feature of sampling-based motion planning is the reliance on an implicit representation of the state space, which is enabled by a set of probing samples. Traditionally, these samples are drawn either probabilistically or deterministically to uniformly cover the state space. Yet, the motion of many robotic systems is often restricted to “small” regions of the state space, due to e.g. ...
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ژورنال
عنوان ژورنال: IEEE Robotics and Automation Letters
سال: 2019
ISSN: 2377-3766,2377-3774
DOI: 10.1109/lra.2019.2893975