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Warehousing and logistics have undergone radical changes and several waves of “the next big thing” over the past several decades, with varying impact. Radio frequency scanners made inventory visible. Voice-directed work made picking hands-free. Early automation and goods-to-person systems brought robots into the mix.
These advances, while powerful, shared a common limitation: they did not really see the warehouse. They followed instructions, processed barcodes and confirmed voice prompts, but they had very little awareness of what was actually happening in three-dimensional space.
We are rapidly entering an era where robots do not simply operate with human commands from static maps and basic point-to-point instructions; they do so autonomously — and with each other.
New warehouses need to be equipped with something like a visual cortex, using depth perception and vision artificial intelligence to understand the world in 3D, and powered by physical AI, which lets them reason about that world and act in it with far more autonomy and, importantly, in real time.
The stakes are significant. Recent analysis puts the global warehouse automation market at about $33.06 billion in 2025, and forecasts it to reach roughly $97.15 billion by 2035, implying a compound annual growth rate of about 10.2% over that period. Separate 2025 research values the global AI in warehousing market at approximately $10 billion to $11 billion in 2024, and suggests it will reach roughly $40 billion to $60 billion by 2030–2032, indicating mid-20% annual growth as more facilities embed AI in robots, software and sensing. Case studies of AI-driven autonomous mobile robots (AMRs) and vision systems in live operations show that these systems can deliver throughput gains of roughly 25% to 40%, reduce inventory and labor costs by 15% to 30% and push picking accuracy to the 99.5% to 99.9% range in high-volume environments.
For supply chain leaders, this is not science fiction. It’s about safer operations, higher throughput and systems that bend rather than break when the business changes. There’s a sense of inevitability — and a lot of hype — around AI, but physical AI is different. It must be safe by design in the way that robots see, plan and move through the same spaces as people.
From Obstacle Avoidance to Spatial Awareness
Most warehouse automation today treats perception as a simple binary problem: Is there something in the way or not? Depth cameras and vision AI change that completely.
One of the key operating requirements is the real-time understanding of the physical environment in which robots operate. That means processing as close to the edge as possible — in this case, the camera itself. By implementing depth map generation at the extreme edge, you not only reduce the downstream processing load, but you also get results in real time, a critical requirement in places where humans and robots work together.
By understanding the environment using both standard imaging and depth data, cameras and techniques like visual SLAM, robots can build a rich 3D understanding of their surroundings. Instead of a flat map with obstacles identified in planes, they see aisles, racks, pallets, totes, people and equipment as objects with shape, distance and motion. Importantly, this enables understanding that some objects are permanent, like walls and racks, and some are ephemeral, like people and temporarily placed pallets.
This leads to smarter mobile robots that navigate crowded, constantly changing distribution centers without depending on floor markers or heavily pre-tuned routes, and that can decide whether to safely go around a pallet left in the middle of an aisle instead of stopping in error.
It also enables more capable robotic arms. Vision-guided arms paired with depth sensors can find the front face of a box in a skewed pallet, avoid plastic wrap reflections, and adjust grip points on the fly, so depalletizing and mixed SKU handling become less brittle and less dependent on perfectly staged loads.
Vision systems create living digital twins. They turn the warehouse into a constantly updated 3D model, not a one-time layout drawing but a live view of where inventory, robots and people are at this moment.
Vision isn’t only embedded within the robots. Fixed-edge devices can be equipped with 3D depth cameras and on-device AI to watch lines and aisles from above. Mounted throughout a plant or warehouse, they create a continuous 3D digital twin of the space that both humans and robots can use for distributed visual intelligence, spatial awareness and flow optimization.
As 3D perception matures, many of these same depth cameras will also support safety use cases, helping robots enforce configurable safety zones around people, slow down or stop as humans approach, and safely share space on the floor alongside lift trucks and pallet jacks. Vision AI platforms, including depth cameras, are already embedded in a large share of AMRs and emerging robotic systems. Instead of redesigning the building to accommodate robots, we’re finally letting robots adapt to the building.
Physical AI: From Tasks to Missions
If the visual cortex is what robots perceive, physical AI is how they decide what to do next.
Today, most robots are given narrow instructions: drive from point A to point B, take a tote from a put wall to a lane, pick a specific item from a specific location.
Physical AI raises the level of abstraction. Instead of scripting every move, we assign missions: Clear pallets from a group of inbound doors, replenish low inventory in a zone before a shipping wave, and keep outbound staging lanes under a target capacity.
A robot, or a fleet of them, can then break that mission into tasks, plan routes and sequences based on current congestion and priorities, and re-plan in real time when something changes, whether it’s a closed door, rush order, or lift truck blocking an aisle.
That is physical AI at work, connecting 3D perception with goal-driven planning in the physical world. Many new warehouse automation projects now use AI for dynamic task allocation, traffic management and exception handling. Instead of automation that stops when reality deviates from the playbook, you get automation that adjusts, much like a good supervisor on a busy shift, but at machine speed and network scale.
All of this depends on powerful 3D perception — robots can’t spot trailer heights, door numbers or nearby workers without trustworthy depth sensing.
Autonomous Mobility as the New Conveyor Belt
Outside the four walls, we already see autonomous mobility in the form of robotaxis and sidewalk delivery bots. Inside and around the warehouse, it is quietly becoming the new conveyor belt.
As 3D vision and physical AI mature, you can expect more autonomous yard operations, with vision-guided tractors spotting trailers at docks and reading door numbers and trailer positions visually instead of relying solely on radio-frequency tags or driver check-ins. Goods will move from inbound trailer to storage to picking to outbound staging with fewer human touches, coordinated by a mix of fixed automation, AMRs, and vision systems. Data from thousands of vision-equipped robots will feed higher-level analytics that suggest layout changes, slotting adjustments or preemptive maintenance to avoid bottlenecks. Large retailers are already proving the model, reporting hundreds of thousands of robots deployed across their networks, with robotics and AI supporting faster fulfillment and significant cost savings.
All of this may sound ambitious, but the underlying technologies — depth perception, vision AI, fleet orchestration, and physical AI planners — are not theoretical. They are in market, maturing quickly, and already deployed in leading warehouses.
For supply chain leaders, a practical path forward is to start with perception-rich pilots where 3D awareness drives mission-level autonomy rather than features, and work toward eliminating complex static workflows.
The long-term vision is clear. Robots will see the warehouse in 3D, understand their goals, know the people around them, and work hand-in-hand with human teams.
Give them scene understanding via a visual cortex built on accurate depth perception, and increasingly capable 3D vision to support productivity and safety. Equip them with physical AI. Connect them to your workforce.
Do that, and you’re not just automating tasks; you’re building a warehouse that can sense, think, and act through whatever the next decade of supply chain disruption brings.
Mark H. Yahiro is vice president of business development at RealSense.
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