Feature: Industrial electronics


Spatial AI: The digital physics and signal integrity enabling human-centric automation


By Dijam Panigrahi, Chief Operating Offi cer and Co-Founder, GridRaster T


he transition to Industry 5.0 – a concept that prioritises a human- centric, resilient and sustainable approach – introduces a critical


challenge for industrial electronics manufacturers: creating a truly symbiotic relationship between intelligent machines and human operators. T is vision hinges on moving industrial automation from rigid, pre-programmed processes to a state of real-world context awareness. For decades, the complexity of deploying


and reprogramming robots, typically reserved for specialised engineers, has made automation uneconomical for high-mix, low-volume production setups. T e high cost and complexity of industrial robotics has been a massive hurdle, particularly for small-to-mid-sized manufacturers.


System engineer’s new challenge T is move to human-centric automation dramatically shiſt s the burden onto


42 March 2026 www.electronicsworld.co.uk


systems engineers. T ey must now design and validate the complex electronic components that underpin this spatial intelligence. T e primary challenge is integrating a dense, high-bandwidth sensor array with an edge processing unit that can perform simultaneous localisation and mapping (SLAM) and semantic segmentation in real time. T is demands unprecedented solutions for signal integrity and EMI immunity, to ensure the reliability of the spatial map whilst maintaining the low latency compute and communication backbone required for safe, collaborative robot control. T e solution lies in a merger of


technologies – specifi cally Spatial AI and mixed reality (MR) – that fundamentally changes the economics and accessibility of industrial automation by shrinking a multi-hour robotics set up to mere minutes. T is shiſt requires a deep dive into the underlying sensor fusion, signal processing and real-time computation necessary to deliver “spatial intelligence”.


Sensor fusion and data ingestion T e core limitation of traditional industrial robotic system is its operational void; it knows the location of its own joints but lacks intuitive understanding of the factory fl oor. Spatial AI solves this by creating a persistent, dynamic and real-time map of the physical factory environment, in eff ect creating a “live” digital blueprint. From engineering perspective the


system relies on two critical subsystems integrated into mixed reality devices (e.g., industrial-grade headsets or powerful tablets):


1. Dense sensor array and industrial requirements These devices must rapidly ingest and process three-dimensional data via sensor fusion. Achieving industrial- grade accuracy requires extreme attention to the capabilities of each sensor type and how their data is registered:


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