Fixed Scanners, Big Gains: How Zebra Helped Royal Canin Boost Loading by 50%

On this episode of Visions: A Machine Vision and Automation Solutions Podcast, VSD’s Head of Content Sharon Spielman talks with Dr. Jimmy Lee of Vention’s Physical AI Lab about using real factory vision data, foundation models, and the GRIP modular pipeline to build robots that can see, understand, and learn in messy manufacturing environments.

In this episode of Visions: A Machine Vision and Automation Solutions Podcast, Cognex CEO Matt Moschner discusses Cognex’s acquisition of RealSense. This transaction marks Cognex’s largest purchase and accelerates a shift toward AI-based, human-like robotic perception. RealSense’s depth-sensing technology and developer ecosystem complements Cognex’s industrial vision portfolio, enabling dynamic, mobile, and autonomous robotics across warehouses and other industries.

In this episode of Visions: A Machine Vision and Automation Solutions Podcast, VSD’s Jim Tatum talks with Dr. Stephan Hrabar, Co-Founder/Chief Strategy Officer for Emessent, about autonomous navigation in GPS denied or deficient areas with Hovermap, Emessent’s solution that utilizes multiple technologies, including SLAM-based LiDAR. They discuss real-world deployments in mines and urban canyons, sensor challenges (dust, smoke, featureless spaces), and the future of multimodal perception and autonomy.

Visions: A Machine Vision and Automation Solutions Podcast Host Jim Tatum interviews Cisco’s Samuel Pasquier about why networks are becoming the foundational infrastructure for physical AI as vision systems and robots move from pilot to production. They discuss Cisco survey findings, key bottlenecks such as latency, bandwidth, lifecycle management, and security, and real-world lessons from manufacturers who had to redesign networks to scale AI.

In this episode of Visions: A Machine Vision and Automation Solutions Podcast, VSD Head of Content Sharon Spielman speaks with Dr. Michael Fanous of Fanous Photonics about the concept of a ”blur budget” — deliberately allowing and engineering motion blur as a tool rather than an error. They discuss continuous motion imaging approaches like GANSCAN and BlurryScope, balancing stage velocity, exposure, optics, and computation, and why training models on real acquisition conditions is critical.