Discrete Motion Planner based on Deep Recurrent Neural Network for Mobile Robot Obstacle Avoidance in Dead-End Environments

Author:

Hoshino Satoshi1,Sumiyoshi Joichiro1

Affiliation:

1. Utsunomiya University,Department of Mechanical and Intelligent Engineering,Tochigi,Japan,321-8585

Publisher

IEEE

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. LiDAR Scan Images for Mobile Robot Motion Planners;2024 IEEE/SICE International Symposium on System Integration (SII);2024-01-08

2. Reduced-Scale Mobile Robots for Autonomous Driving Research;IEEE Transactions on Intelligent Transportation Systems;2024

3. Mobile Robot Motion Planning through Obstacle State Classifier;2023 62nd Annual Conference of the Society of Instrument and Control Engineers (SICE);2023-09-06

4. A non-uniform quadtree map building method including dead-end semantics extraction;Green Energy and Intelligent Transportation;2023-04

5. End-to-End Motion Planners Through Multi-Task Learning for Mobile Robots with 2D LiDAR;2023 IEEE/SICE International Symposium on System Integration (SII);2023-01-17

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