Papers
6
Total Citations
114
H-Index
5
About
Yao Yeboah is a robotics researcher whose work centers on autonomous navigation, visual tracking, and control systems for mobile robots. His most impactful contribution, “A Robust Model Predictive Control Strategy for Trajectory Tracking of Omni-directional Mobile Robots” (2019), has garnered 86 citations, establishing a foundation for precise motion control in complex environments. Yeboah has also advanced visual tracking for robotic vision systems, addressing challenges like incessant object and robot motion with robust detection frameworks. In indoor navigation, he pioneered the use of Siamese deep convolutional neural networks to reduce reliance on manual scene labeling and multi-sensor fusion, lowering computational costs while improving robustness. His work on semantic scene segmentation further enables collision-free navigation for hardware-constrained robots. Notable achievements include designing the WeLCH hexapod robot for glass screen wall inspection, integrating walking and climbing capabilities. Yeboah’s multi-objective deep CNN for outdoor auto-navigation, though with fewer citations, demonstrates his forward-looking approach to vision-based multi-agent coordination. His research consistently pushes the boundaries of autonomous robotics, blending deep learning with practical control solutions.
Research Focus
Key Achievements
Top Papers
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- 5The System Design and Gait Planning for Walking-Climbing Hexapod5 citations · 2019
- 6Multi-Objective Deep CNN for Outdoor Auto-Navigation2 citations · 2018