Brandon Johns
Papers
3
Total Citations
17
H-Index
2
About
Brandon Johns is a researcher at the forefront of robotics and automation, with a particular focus on construction and human augmentation. His work addresses critical safety and efficiency challenges in high-risk environments. Johns’ most impactful contribution to date is his 2025 paper on physics-informed neural networks for load sway prediction in autonomous mobile cranes (10 citations), which tackles a fundamental instability issue that threatens crane operations and surrounding infrastructure. This work bridges deep learning with physical models to offer more accurate, real-time predictions than traditional methods. Earlier, in 2020, he co-authored a critical review of automation solutions for curtain wall installation in high-rise buildings (5 citations), identifying key gaps and opportunities for robotic integration in construction. Demonstrating a broader vision for human-robot interaction, Johns also developed a low-cost wearable robotic arm designed to augment human capability during overhead reaching tasks (2019, 2 citations). His research consistently targets the intersection of safety, automation, and practical deployment, making significant strides toward safer, more efficient construction sites and human-robot collaborative systems.
Research Focus
Key Achievements
Top Papers
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