Max Robotics — Global Service Robot Directory + US Certification

About

Justin Cano’s research lies at the intersection of ultra-wideband (UWB) communication, multi-robot systems, and high-precision indoor localization. His work addresses a fundamental challenge in robotics: how to achieve accurate, real-time position estimation in GPS-denied environments. Cano’s most influential contribution is a Kalman Filter-based algorithm for simultaneous time synchronization and localization in UWB networks (44 citations), which enables decimeter-level positioning even in challenging multipath conditions. He further advanced the field by identifying and correcting clock and power-induced biases in UWB time-of-flight measurements (23 citations), a critical step toward practical, low-cost navigation for mobile robots. Beyond hardware-level improvements, Cano has developed rigorous theoretical frameworks for motion planning that optimize network geometry to maximize localizability. His work on rigidity-constrained CRLB-based planning (8 citations) and distance-deteriorated relative measurements (3 citations) provides actionable strategies for maintaining high positioning accuracy as robots move and measurement quality degrades. Together, Cano’s contributions bridge the gap between theoretical localization bounds and real-world robotic navigation, making him a key figure in the development of reliable, infrastructure-free positioning systems for autonomous multi-robot teams.