Papers
3
Total Citations
9
H-Index
2
About
Kaiwen Wang is a researcher at the forefront of autonomous robotics, specializing in simultaneous localization and mapping (SLAM) and task offloading for networked robotic systems. His work bridges critical gaps in deploying robots in challenging, real-world environments—from sprawling construction sites to satellite-terrestrial communication networks. Wang’s major contributions include developing and evaluating LiDAR SLAM algorithms tailored for construction robots operating in large public building sites, where he tackles persistent issues like point-cloud drift and z-axis errors to enable reliable autonomous navigation for safety inspection. His research demonstrates significant impact, with his most-cited papers—such as “Evaluation of LiDAR SLAM algorithms for construction robots in large public construction sites” and “Task Offloading in MEC-Aided Satellite-Terrestrial Networks”—each garnering 4 citations, reflecting growing interest in his practical solutions. Notably, his work on reinforcement learning for task offloading in hybrid networks addresses the critical challenge of supporting computation-intensive, latency-sensitive tasks for wide-area robots. By advancing both ground-level SLAM and network-level optimization, Kaiwen Wang is helping to shape a future where robots can operate autonomously and efficiently across diverse, demanding environments.
Research Focus
Key Achievements
Top Papers
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