Papers
5
Total Citations
27
H-Index
4
About
Pingan Wang is a robotics researcher whose work bridges agricultural automation and assistive medical devices. His primary research areas include robotic perception, precision agriculture, and exoskeleton technology. Wang’s most impactful contribution is an enhanced YOLOv8n deep learning model for apple detection, localization, and counting in complex orchard environments—a system designed to enable robotic arm-based harvesting. This work, published in 2025, has already garnered 9 citations and demonstrates significant performance improvements over standard models like YOLOv5 and YOLOv6. He also developed an automatic beehive transporting system using YOLO and DeepSORT algorithms (6 citations), showcasing his versatility in applying computer vision to real-world agricultural challenges. Earlier in his career, Wang contributed to human motion prediction for the Non-binding Lower Extremity Exoskeleton (NBLEX), a novel approach that frees pilots from attachment-based systems to enhance safety. His four-legged gait planning method for mobile medical exoskeletons further supports spinal cord injury patients in regaining mobility. With a growing portfolio that includes integrated robotic harvesters combining real-time perception and path planning, Wang’s work is shaping the future of both agricultural robotics and rehabilitation engineering.
Research Focus
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Top Papers
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