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Automation has revolutionized the way warehouses, distribution centers and manufacturing organizations support human workers and accomplish tasks. While adoption is still in its infancy, ABI Research estimates four million robots are expected to be added to warehouse environments by 2025, with Automated Guided Vehicles (AGVs) and Automated Mobile Robots (AMRs) leading the charge.
These industrial self-driving vehicles will help thousands of organizations streamline mundane, repetitive and often dirty and dangerous tasks. And, while AMRs offer increased flexibility compared to their line-following AGV counterparts, both generally perform operations at the same pace and throughput with variability only due to external factors.
So, what would it take for a robot to improve over time? Artificial Intelligence (AI). AI-powered orchestration engines deliver unparalleled abilities to collect and uncover insights based on robot-specific data. This information can be used to fuel improvements, enhance real-world performance and even adapt future designs.
Edge Case Impossibility
According to the 2020 MHI Annual Industry Report, only 12% of businesses are using AI technology in their warehouses. This number is expected to reach more than 60% in less than six years. The reason for this unparalleled growth is the efficiency AI unlocks by enabling continuous improvement.
Today, most AGVs and AMRs are developed without AI factored into their design. Instead, they are built to a complex set of requirements that attempt to address every edge case that could arise in a highly dynamic environment. As anyone in the logistics industry knows, this is a near-impossible task. Warehouses are complex ecosystems, continually changing with new delays, bottlenecks, and needs every day.
As these robots learn more about their environments and perform simple, repetitive tasks without the need for intervention, humans can focus on higher-level tasks, which leads to improved rates of employee satisfaction and morale.
Continuous Improvement
A self-driving industrial vehicle equipped with AI, on the other hand, can learn on the job and adapt to these variables. By quickly deploying these vehicles to automate core warehouse functions, organizations benefit from onboard engines that collect, analyze, and learn from travel and production data. Warehouse operations teams can then review site-specific data to improve workflows and daily task allocation. Not only will it improve speed, efficiency and throughput during deployment, but this information can also help inform vendors when engineering new systems in the future.
From a more practical standpoint, though, AI can help solve real-world challenges where manual equipment falls short. For instance, most warehouses contain different types of pallets from vendors all over the world. While some pallets contain normalized dimensions, it is unlikely to have every one made according to a single design. So in a 300,000 pallet position warehouse, you could have 1,000-2,000 different designs.
Self-driving industrial vehicles without AI support will pick up many of the designs, but if one falls out of spec, the machine will need human intervention. However, AI-enabled AMRs and AGVs can use sensors and past human intervention to learn the best way to pick and handle these new pallets. The continuous learning leads to a progressive decrease in human intervention with an increase in efficiency.
As these robots learn more about their environments and perform simple, repetitive tasks without the need for intervention, humans can focus on higher-level tasks, which leads to improved rates of employee satisfaction and morale.
The addition of AMRs and AGVs can even lead to new career opportunities as management, configuration and deployment open up roles in warehouses.
Keeping Workers Smiling
The industry is slowly beginning to understand that robots are not here to take jobs, but rather help improve the human experience. In fact, many organizations, such as MiltonCAT, have recognized how much AMRs assist their human counterparts delegate work on demand, decrease non-value-added travel, and focus on value-added tasks.
The addition of AMRs and AGVs can even lead to new career opportunities as management, configuration and deployment open up roles in warehouses. As an example, we recently saw a large retail customer develop an advanced role of Automation Associates. These employees, once tasked with moving goods, are now responsible for higher level tasks like assigning missions to AMRs, tracking progress, looking for ways to improve processes, and handling edge cases. The job provides new challenges and experiences, with managers reporting renewed upbeat and energetic approaches to work.
The key, once again, to keeping these employees happy comes down to AI. Without the added intelligence, employees can become frustrated troubleshooting jobs designated for robots to complete. This can also lead to decreased production as multiple agents will be designated to solve a single task.
AI Fundamental
As we look toward the future, AGVs, AMRs and other automation solutions will simplify tasks and improve throughput for organizations worldwide. To unlock robots’ true potential and keep their human counterparts happy, warehouse managers need to look for solutions where AI is a fundamental building block in robotics design and day-to-day management.
About the Author
Denis Lussault, Vice President of Autonomy, Vecna Robotics
Denis Lussault is Vecna Robotics’ Vice President of Autonomy. In his role, Lussault is responsible for overseeing the company’s autonomy division and meeting the growing demand for world-class robotic platforms and autonomy software that simplify customer workflows. Most recently, Lussault served as Vice President of Americas at Balyo where he previously managed European operations. Before that, he served in leadership roles for several companies, including Atlantic Technologies and Renault-Nissan-Mitsubishi. Lussault holds a master’s degree in mechanical engineering from the Ecole Nationale Supérieure d’Arts et Métiers and a Master of Business Administration from Massachusetts Institute of Technology.
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