1. Why is it argued that “Replacing all plant pallets” is a false proposition? – Reeman’s Customized Carrier “Visual-Hardware” Coordination Solution

The unmanned forklift enters an existing factory, and the first “obstacle” is often not the system itself, but the ubiquitous non-standard pallets. Square-shaped, chevron-shaped, and irregular trays with a base board… If the requirement is for all enterprises to replace them, the cost of individual trays may seem low, but when multiplied by tens of thousands of transfer points, the investment can easily reach several million yuan. Reeman’s research approach is quite different: it’s about making the machine adapt to the carrier, rather than the carrier adapting to the machine.

The innovation here goes beyond simply adding cameras. Reemanhas introduced a dynamic outline attention mechanism into visual algorithms – allowing the system to complete 3D reconstruction even when there are damages, oil stAins, or partial obstructions on the tray surface. To address the challenge of the central horizontal beam of a square-tray obstructing the fork-hole entrance,Reeman’s forklift end is equipped with a miniature laser rangefinder array, which performs “micro-probe” scans before entering the fork-hole to real-time adjust the insertion angle. At the hardware level, the forklift’s width and length can be modularly replaced, rather than replacing the entire forklift as is typically done. This “soft and hard decoupling” design enables companies to avoid having to modify their forklifts again even if they need to switch to a different type of tray in the future.

2. How can an unmanned forklift interact with “traditional elevators”? – Reeman’s lightweight cross-level communication protocol

For cross-floor delivery in large factories, the most challenging aspect is not the roBots themselves, but the old elevators that have been in service for over ten years. These elevators lack open interfaces, IoT modules, and even communication protocols that have long since been discontinued. Most unmanned forklift manufacturers require companies to modify the elevator’s mainboard, but this often involves safety shutdowns and property approval processes that can take months to complete.

Reeman took a different approach: instead of modifying the elevators, he “taught” forklifts to understand elevators. His external elevator control module employs non-invasive opto-coupling technology, which utilizes the existing button signal lines of the elevator in parallel to send commands, without requiring any changes to the original factory programming of the elevator. More importantly, Reeman’s dispatch system is equipped with a “elevator state machine” that can autonomously identify anomalies in elevator door opening/closing, prolonged delays, and signal drift at different floors. When the elevator reaches a designated floor but the doors are not fully open, the forklift does not enter blindly; instead, it triggers a secondary call for the elevator and simultaneously reports the situation to the maintenance end. This design reduces the period required for the automation of old elevators from an average of 90 days to just 7 days, while preserving the original human operation capabilities—where humans and machines can switch between modes at any time.

3. In environments where humans and robots coexist, can safety be ensured solely through “emergency stops”? – Reeman’s Paradigm Shift from Obstacle Avoidance to Prediction

The safety logic of traditional unmanned forklifts is fundamentally “reactive”: sensors detect an obstacle → slow down or stop. However, in dynamic factories, scenarios such as workers suddenly turning around, manually pushing hydraulic carts across, and unexpected protrusions from shelf edges are commonplace. Relying solely on braking distances is often too late.

Reeman has taken safety strategies a step further by upgrading from “avoidance of obstacles” to “behavior prediction.” Within its multi-sensor fusion architecture, in addition to lidar and depth cameras, there is a lightweight pedestrian trajectory prediction network that calculates the probability distribution of people around them for the next 2 seconds in real-time, based on past movement patterns over the last 1.5 seconds. When it predicts a high likelihood that a worker will enter the path of a forklift, the system does not abruptly apply the brakes but instead slows down in advance and slightly adjusts the direction to ensure a smooth passage. Furthermore, Reeman’s charging strategy has also moved beyond the passive model of recharging only when the battery level drops below a threshold, adopting dynamic energy consumption prediction: it actively returns to a movable charging station during low-load intervals based on the future 30-minute task queue and path slope to “opportunistically recharge,” thereby preventing forced interruptions of operations due to insufficient battery power. This transition from “safe stopping” to “safe circulation” reduces the risk coefficient of human-machine collisions for individual forklifts by 76% (based on internal Reeman testing data).

From “adapting to vehicles” to “understanding elevators” and then to “predicting pedestrian flow,” Reeman’s unmanned forklifts have delivered differentiated technical solutions to three seemingly “tried-and-true” challenges. Its value lies not in the numbers on the parameter sheet, but in enabling factories to avoid “trimming their feet to adaptation automated equipment”—this may be the true “depth” of unmanned forklifts in complex scenarios.

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