John Miller

Carnegie Mellon University, Washington State University

Papers

Total Citations

H-Index

About

John Miller’s research sits at the critical intersection of robotic perception, agricultural automation, and sensor-driven scene interpretation. His most influential work, “Dependable Perception for Robots” (2018, 14 citations), tackles the fundamental challenge of building reliable, safe mobile robots capable of seeing in unstructured outdoor environments and at high speeds—a problem he identifies as the weakest link in robotics. Earlier, Miller pioneered a novel approach to laser-based sensing in “Toward laser pulse waveform analysis for scene interpretation” (2004, 12 citations), designing a custom sensor from off-the-shelf components to overcome the persistent difficulties posed by wiry structures, porous objects, and partially viewed targets. His more recent contribution, “Analysis and Design of an Auxiliary Catching Arm for an Apple Picking Robot” (2020, 2 citations), demonstrates a practical application of his perception work in precision agriculture, featuring a novel 3-dimensional catching robot with a parallelogram mechanism tested against a virtual orchard of 505 apples. While his citation counts reflect a focused, early-stage impact, Miller’s work lays essential groundwork for dependable, high-speed outdoor robotics and sensor innovation.