The PUDU MP2000 is designed to simplify autonomous floor-to-floor material transport across warehouses and factories. | Credit: Pudu Robotics
With pallet pickup cycles as fast as 20 seconds, Pudu Robotics yesterday unveiled the MP2000, an AI-powered autonomous pallet handler built to tackle high-frequency, heavy-payload transport in crowded manufacturing and logistics hubs.
The MP2000 extends the company‘s portfolio of autonomous mobile robots (AMRs), enabling autonomous pallet acquisition and drop-off functions in a warehouse or factory setting.
Equipped with 3D lidar and depth cameras, the MP2000 can identify target pallet locations and adjust approach paths in real time. The product promises fork-in cycles as low as 20 seconds.
Extended perception and obstacle avoidance
With onboard perception, the MP2000 safely navigates through a facility from pickup to dropoff, avoiding obstacles along the way. The sensors can also validate pallet size/configuration, adjusting for pallet misalignment and load dimensions to navigate tight spaces smoothly.
Pallet handling rarely follows a single standard. PUDU MP2000 supports common pallet formats, including three-runner pallets and perimeter-base pallets, while also adapting to customized, non-standard load carriers.
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When combined with other Pudu Robotics AMRs in the facility, distributed fleet intelligence combines local decision-making with fleet-level coordination. The fleet manager also supports multiple robot task coordination during network outages.
Plus, the vehicle allows manual operator override to turn the vehicle into a powered manual forklift.
Pudu’s T Series AMRs, including PUDU T300, address flexible delivery and material transport. PUDU MP2000 extends this capability into palletized, heavier-load and floor-to-floor transportation, covering core workflows across loading docks, warehouses, line-side storage and production workshops.
The MP2000 communicates with peripheral equipment such as doors and elevators to enable access to controlled areas of the facility.
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