Deep Learning-Based Complete Coverage Path Planning With Re-Joint and Obstacle Fusion Paradigm

Deep Learning-Based Complete Coverage Path Planning With Re-Joint and Obstacle Fusion Paradigm

With the introduction of autonomy into the precision agriculture process, environmental exploration, disaster response, and other fields, one of the global demands is to navigate autonomous vehicles to completely cover entire unknown environments. In the previous complete coverage path planning (CCP...

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Bibliographic Details
Main Authors: Tingjun Lei, Chaomin Luo, Gene Eu Jan, Zhuming Bi
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-03-01
Series:Frontiers in Robotics and AI
Subjects:
Deep learning-based path generation
complete coverage path planning
obstacle approximation and fusion
nature-inspired path planning
velocity-based local navigator
re-joint paradigm
Online Access:https://www.frontiersin.org/articles/10.3389/frobt.2022.843816/full

Internet

https://www.frontiersin.org/articles/10.3389/frobt.2022.843816/full

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