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Home » MODEX 2020: AutoGuide debuts highest payload autonomous mobile robot tugger

MODEX 2020: AutoGuide debuts highest payload autonomous mobile robot tugger

Max-N15 is capable of transporting up to 15,000 pounds, company says.

AutoGuide MaxN15
March 9, 2020
DC Velocity Staff
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AutoGuide Mobile Robots launched the Max-N15, the company’s highest payload modular autonomous mobile robot (AMR)—a tugger capable of transporting up to 15,000 pounds—in booth 3403 at MODEX 2020 this morning. 

Built on AutoGuide’s patent-pending modular design, the Max-N15 provides a common base that can be adapted for multiple manufacturing and warehouse material handling tasks with different attachments to create lift trucks, tuggers or high bay pallet lift trucks, explained Rob Sullivan, president and CEO.

“With the Max-N15, companies needing to pull heavy payloads can safely and easily do so with autonomous robotics, greatly improving operational safety and enabling labor resources to be utilized for higher value tasks,” Sullivan said. “Our advanced, flexible, modular platform means companies no longer need to purchase completely different systems for different applications.”

Instead, he continued, customers can transform one vehicle (or a fleet of vehicles) with different attachments to accommodate their ever-changing work environments. “The fork truck you’re using today can easily be transformed into a tugger tomorrow, and then back again the next day,” he said.

Additionally, AutoGuide’s vehicles (as well as robots from other manufacturers) are controlled with the company’s SurePath fleet management software. The software provides an easy means to specify routes, coordinate autonomous lifting and transport of pallets, and manage AMR traffic to optimize load handling and transport, Sullivan continued. 

He added that SurePath can integrate with existing manufacturing execution systems (MES), warehouse management software (WMS), warehouse execution software (WES), and other systems to coordinate vehicle routing with inventory movement needs, determining the shortest travel path for more optimized throughput.

 

 

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