Papers
11
Total Citations
150
H-Index
7
About
Yingying Yu is a leading researcher in intelligent robotics, specializing in vision-based robotic grasping, multi-robot coordination, and autonomous navigation. Her most impactful work introduces a two-stream convolutional neural network that simultaneously performs object detection and segmentation for robotic grasping, achieving 38 citations and setting a new standard for handling background interference in manipulation tasks. Yu further advanced the field by developing a vision-based grasping method that overcomes occlusion challenges through an SSD-based detector and an innovative image inpainting and recognition network (25 citations). Her contributions extend to large-scale multi-robot systems, where she proposed a hierarchical task allocation approach with resource constraints (25 citations), enabling efficient coordination among numerous robots. Yu’s research on robot navigation incorporates situational awareness, combining scene prediction and interpretation with topological mapping for autonomous movement. With over 150 total citations across her publications, Yu has established herself as a key innovator in robotic manipulation and multi-agent systems. Her work on human-following robots using binocular cameras and collision avoidance in unknown environments further demonstrates her versatility. Yu’s integrated approaches to perception, grasping, and coordination continue to influence both academic research and practical robotic applications.
Research Focus
Key Achievements
Top Papers
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- 4Robot Navigation Based on Situational Awareness11 citations · 2021
- 5A human-following approach using binocular camera11 citations · 2017
- 6A Vision-Based Robotic Grasping Approach under the Disturbance of Obstacles10 citations · 2018
- 7A Grasping CNN with Image Segmentation for Mobile Manipulating Robot8 citations · 2019
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