Vecna’s self-driving vehicles are a unique blend of AMR and AGV technology. They are able to handle the capacity of bulk payloads and autonomously navigate to optimize runtime and throughput. They also use machine learning to improve over time and update the same way a smart phone would with a new software release, which significantly delays depreciation and the need to invest in new robots before achieving a substantial return on investment.
Here are the technological advances of the Tugger, made possible by engineers dedicated to customer success.
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In the quest for more autonomy, auto-hitching has long been requested by companies seeking robotic solutions to reduce the need for human intervention. Several companies have come to the table with their versions, but none as robust as the one developed by the team at Vecna. The team spent time making sure the solution was compatible with the widest range of cart configurations so customers may not be required to purchase all new carts and equipment.
The average time to hitch to a cart is 15 seconds, which greatly reduces the time previously spent waiting for a worker to step in and engage the mechanism to hitch a cart. This feature also provides more flexibility to the places a cart can be picked up and dropped off because it doesn’t require a worker to be waiting at each station.
Daniel Theobald and Mike Baier conceptualized the solution and Felipe Depine led the development team. “Customers want to work with partners who proactively meet their needs,” Daniel said, whose direction is always customer-centric. Felipe added, “We wanted to find not just a solution, but the best solution and test it thoroughly.”
To help customers achieve more continuous operations and higher uptime, the 5th generation self-driving Tugger received a world-class upgrade with its automatic charging capabilities. Jay Doyle led the team in designing and developing a module that meets customers at their point of need.
This feature takes advantage of opportunity charging, which means the charging stations are located where trucks naturally stop in their routine, like during loading/unloading. The battery charges very quickly in just mere seconds during these stops. The team calculates the amount of charging units needed to support fully continuous operations in the workflow, and works with the customer to implement a solution that works best in that facility whether it’s an in-ground, free-standing, or creative solution.
The feature also allows the Tugger to charge at the most opportune times in its workflow, like during downtime or taking routes that reach charging stations more frequently, further reducing the need for human intervention. “If there’s a way to shave time down to milliseconds, we’ll develop and test it,” said Jay of the team’s ambition.
The technology driving the Tugger’s navigation and dynamic obstacle avoidance has been developing and advancing for the past 20 years. Many of the team members led by Zac Dydek have been working on it longer than other startups have been in business, creating the most advanced navigation in the industry.
The secret has been to make the robot think like a person faced with a task, not a programmed computer. It allows the robot to react to its environment in realtime more accurately in unexpected situations than other AGVs/AMRs in the market.
The team members developing this technology are experts in vision and motion planning control. Their personal passions in these systems have made the Tugger’s navigation a standout for those incorporating automation in even the toughest environments where other AGVs/AMRs have failed.
Ashwin Thangali has a PhD in computer vision and helped design the foundations of this technology from the start. He explained, “With the many different types of environments our robots operate in and the advances in sensor technology, we’re constantly improving on navigation. We all have a sense of responsibility and ownership of this technology here, and we’re proud to be at the leading edge of advancements.”
One of the most impressive features of Tugger navigation is dynamic obstacle avoidance. Thanks to the human-like way it assesses situations, the Tugger avoids dynamic and unexpected static obstacles smoothly. Zac shared, “A warehouse with human workers is naturally going to have unexpected obstacles, and this feature allows the Tugger to uniquely operate smoothly in a cluttered or highly dynamic environment.”
The story behind this feature’s success is noteworthy! In its early days, Vecna was integrating delivery robots in hospital environments, which are not only chaotic but obstacles vary greatly in shape and behavior. The robot needed the ability to see and operate safely around wheelchairs, hospital beds, people running, casts and canes, and children – all which behave and appear differently in sensors.
“Our team found the right mix of sensors and vision technology to accurately and safely operate in such chaotic environments, laying a strong foundation for the technology to evolve for industrial applications,” said Ashwin.
To learn more about the capabilities and specifications of the Tugger, visit this page.