CommonSense Robotics Breaks Ground on World’s First Underground Automated Warehouse

Recommendation: Launch a staged deployment to leverage machine and data networks to maximize inventory visibility underground, reducing handling time and consumption while delivering reliable throughput for micro-fulfilment centers.

What makes it work: Advancements in automated warehousing have been launched to operate in a subterranean footprint, pairing machine intelligence with robust networks. Real-time data enables task assignment, collision avoidance, and energy-conscious routing, helping to protect inventory and sustain throughput during peak demand.

The system concentrates on micro-fulfilment that taps into resources by consolidating multiple channels into a single underground hub with a tight network. By reducing surface travel, the design cuts consumption and uses networks to coordinate inventory across stations. This alignment creates new ways to balance inbound supply with outbound demand, turning data into action and turning resources into predictable flow for the future.

Executives anticipate that these moves will shape a blueprint for urban logistics, turning underground space into a scalable platform where advancements in automation, backed by machine data, map new workflows and shorten cycle times. This approach gives them flexibility to scale operations while preserving inventory, respecting capacity limits, and expanding micro-fulfilment networks beyond surface warehouses.

How Micro-Fulfilment Centres Optimise Their Energy Usage and Resource Management

Adopt a centralized energy-management platform that automates charging, materials handling, and replenishment for robotics fleets at micro-fulfilment centres, leveraging robotic systems to keep throughput steady. Plan to automate repetitive tasks where possible. This approach ties energy-efficient workflows to a technology-driven operation, and a partnership with utilities and technology providers helps rural sites integrate on-site solar and storage, aligning with integrating patterns of demand to support online orders.

According to pilots, energy use per order fell by 20-35% when charging aligned with off-peak windows and robotics-enabled automation reduces several trips across picking zones. Experts said this shift reduces energy draw during peak hours and supports stable throughput across centres, while also reducing travel for workers and outside vehicles.

Integrating demand signals with inventory placement improves optimal use of space and energy. In rural settings, smaller garages become micro hubs, where a meir proposition places high-demand items close to customers to cut travel by cars and match sparser demand patterns.

To sustain gains, establish online support dashboards, track energy intensity, and review resource use against seasonal patterns. Many networks benefit from a technology-driven loop that links robotics maintenance, energy procurement, and data analytics, creating an efficient proposition for micro-centres.

What Energy Sources Power Underground Micro-Fulfilment Centres?

Power underground micro-fulfilment centres with a grid-linked base and a modular BESS (1–3 MWh, 0.5–1 MW) for minimising peak charges. This approach supports a stable, innovative energy supply that keeps robotics running during high-demand periods while curbing energy spend across products.

Adopt a strategy that leverages online energy procurement and a partner like goren to access renewable energy, lowering overall costs while promoting profitability across many orders and products. The energy mix should be sized to cover last-mile robotics activity and to handle order spikes; thats a practical rule for steady operations.

Inside the facility, an energy-management system coordinates charging for several robots and automated arms, aligning with learning feedback to reduce wasted energy. The fitting of BESS, grid tie, and fallback codes ensures resilience and lower risk for critical order fulfilment for the company.

Overall, this approach supports a lean cost profile and faster throughput. Establish clear codes and reporting standards for energy performance, maintain safe operation, and iterate from field data to optimise each installation.

How to Minimise Cooling Loads in Subterranean Hubs?

First, install optimised insulation and airtight building envelopes in the spaces, using phase-change materials and reflective lining to cut heat gains. These sustainable methods reduce cooling loads by up to 40% in subterranean hubs. In the first phase, pilot the cooling loops at a small scale to validate temperature margins, and then scale across the network. Second, deploy a network of interconnected cooling loops that leverage ground-source or seawater cooling through a closed system; this will promote stable temperatures with lower energy use, and helps establish optimal balance across the network. Inside the spaces, locate equipment and goods with careful locating near demand points to minimise long transit and heat contribution, and balance the load across the network. What matters is controlling peak loads during warm hours by shifting demand to cooler periods and using free cooling when external conditions allow. Establish a control system across other bays and operations to share capacity and avoid running multiple chillers at partial load. This approach, using technology and micro spaces, reduces energy intensity and supports sustainable operations while servicing goods across the interconnected micro city network.

How Can Waste Heat be Recovered and Reused On Site?

Install a closed-loop waste heat recovery system that captures heat from conveyors, motors, compressors, and condensers and uses a plate heat exchanger to preheat incoming process water to 60–70°C for wash stations and space heating. This approach can offset 25–40% of on-site heating energy, with a typical payback of 2–4 years, depending on local energy prices. Pair the exchanger with a thermal storage tank and a variable-speed pump to deliver heat efficiently and to store energy for peak demand periods.

Identify heat sources by a two-week survey across the facility’s equipment and operations. Classify heat by temperature: high-grade above 60°C from boilers and condensers; medium-grade 40–60°C from VFD-driven motors and pumps; low-grade below 40°C from ventilation. Use data loggers to quantify recoverable energy and map applications such as domestic hot water, space heating, and process preheating. According to shalom aviv, the engineer leading the initiative, focusing on medium-grade heat often yields the quickest gains while keeping the system scalable for the next expansion.

Design the system around a primary hot-water loop fed by the waste heat, with plate heat exchangers to transfer heat to a glycol or water secondary loop. Using a glycol secondary loop provides winter protection and stable temperatures. A heat pump can elevate low-temperature waste heat to a usable level when ambient conditions or demand peak. Integrate a thermal storage tank to decouple heat generation from demand, improving efficiency and enabling stable operation during high-demand periods. All components should be sized for continuous uptime in an underground environment, with fault-tolerant pumps and automatic valve controls.

Next, establish a cross-functional project team–operations, facilities, energy, and IT–to identify opportunities, set KPIs, and define codes and safety requirements. Build a phased plan: in the second phase, scale to other zones and order the rollout to maintain consistency; year two aims to harmonise with fulfilment workflows and demand chains, ensuring the system supports sustainable operations and strengthens customer confidence and supply stability.

Economics and governance come next. Use a life-cycle cost model to compare capital with running costs, account for maintenance, and estimate annual savings in kWh and CO2 reductions. Expect capital expenditure in the range of a few hundred dollars per kW of recovered heat, with yearly savings depending on energy prices. Monitor performance via the building management system and set alerts for temperature, flow, and heat-exchanger efficiency to ensure consistently efficient operation. This approach can revolutionise energy handling within the facility, creating resilient demand chains that support customers while advancing sustainability goals across the site.

Which Real-Time Analytics Guide Energy Use in Micro-Fulfilment?

Implement a per-lane energy model by predicting motor load and cooling needs every 30 seconds, then automate controls to modulate conveyor speeds, adjust clearance, and reallocate space to meet demand without waste. This proposition lets managers compare configurations quickly and easily, supporting a clear strategy to slash peak energy use while preserving delivery performance.

Situated sensors are placed at key nodes along lanes and in storage zones to monitor temperature, humidity, live current, and door states. Feed data into a real-time analytics core that outputs actions: reduce fan speed in idle zones, dim lighting in sparser areas, and shift energy to high-demand lanes.

Link the model to on-demand delivery windows and present a straightforward proposition to operators: let energy actions run automated, with dashboards showing usage and delivered orders.

Run a 60-day pilot in a facility with 4-6 lanes, install the ecosystem, and calibrate the model against a 30-day baseline. Expect energy reductions in refrigeration and lighting of 12–20% while keeping accuracy and delivery delay within acceptable bounds.

Looking ahead into the future, scalable analytics will handle growing space and demand, enabling seamless expansion for advanced micro-fulfilment networks. Space usage becomes smarter, the delivery cycle shorter, and the energy footprint reduced as predicting signals drive automated adjustments. For example, super-pharm can cut refrigeration energy by 15–25% by aligning temperature setpoints with demand forecasts. This approach helps grow efficiency across sites.

How Should Automation Scheduling Balance Shifts and Energy Demand?

Adopt a dynamic, energy-aware scheduling model that aligns shift plans with real-time grid demand and price signals. Prioritize off-peak automation tasks and reserve peak hours for essential operations, reducing energy cost by 15-25% in typical warehouses while maintaining service levels.

• What’s the strategy for balancing shifts and energy demand? Segment the day into energy windows and assign high-load tasks (takeoff, sorting, heavy lifting) to off-peak periods. Use forecasted demand and price signals to drive start times, cutting peak consumption by 20-30% and smoothing workload across inside facilities.
• Use several interconnected fleets of autonomous units – cars, vans, and fixed-robot systems – to create a flexible playground where tasks migrate between vehicles and robots as demand shifts. This enables reduce in idle time and optimises utilisation of vehicles across rural and urban nodes.
• Leverage advancements in methods like predictive scheduling and energy-aware routing. These enables the system to locate vehicles near high-demand customers and downtune activity when prices spike, maintaining service levels for customers without wasteful energy use.
• Implement a two-tier planning cycle: an upstream strategy that sets daily shift blocks, and a downstream micro-scheduling loop that adjusts inside shifts in response to live data. This approach supports long-distance transfers and rapid takeoff when demand surges and avoids overmatching capacity to fluctuating loads.
• Foster collaboration with partner companys and suppliers to share capacity and align charging, maintenance, and loading windows. A shared energy-aware calendar helps rural hubs and city centers coordinate vehicle locating and task handoffs, reducing peak strain on the grid.
• Track key metrics: energy intensity per order, peak-hour energy reduction, average task duration, and on-time delivery rate. Aim for a 12-18% improvement in fleet energy efficiency within the first quarter after deployment, with incremental gains as models learn from real cycles.
• Establish guardrails to prevent overburdening workers while automation handles repetitive tasks. Maintain human oversight for exception handling, quality checks, and safety, ensuring many tasks remain aligned with customer expectations and urban-rural distribution patterns.

Inside the control tower, use a centralized dashboard to compare scenarios, from a conservative baseline to an aggressive off-peak strategy. The system simulates several future days, showing how energy demand, takeoff events, and vehicle utilization shift with each adjustment. This supports a pragmatic balance between cost, reliability, and speed, and it helps teams communicate with customers about expected wait times. Shalom to the teams implementing this, and to the users who benefit from steadier service with smarter energy planning.