Papers
5
Total Citations
58
H-Index
4
About
Yonglong Luo is a researcher specializing in mobile robotics, with key contributions in indoor object recognition and autonomous navigation. His work bridges deep learning and path planning to enhance robot perception and movement in complex environments. Luo pioneered the use of pre-trained convolutional neural networks (CNNs) for indoor object recognition, developing a prior knowledge-based deep learning method that significantly improved detection precision for mobile robot navigation—a foundational approach cited over 22 times. His 2017 paper on CNN-based indoor object detection, with 15 citations, established a pipeline that leverages both public and private datasets for robust recognition. In path planning, Luo advanced real-time obstacle avoidance in dynamic environments, addressing traditional limitations in computational efficiency and optimization. His 2014 and 2023 papers on improved path planning algorithms, collectively cited 21 times, offer solutions for mobile robots navigating unpredictable settings. Luo’s work has practical applications in autonomous systems, from warehouse logistics to service robotics, and his integration of perception and planning continues to influence the field.
Research Focus
Key Achievements
Top Papers
- 1
- 2Indoor object recognition using pre-trained convolutional neural network15 citations · 2017
- 3Improved path planning algorithm for mobile robots10 citations · 2023
- 4
- 5Improved path planning algorithm for mobile robots3 citations · 2022