Optimizing Autonomous Mobile Robots: Seegrid’s Journey with Simio Simulation

Introduction

In the rapidly evolving landscape of material handling, Seegrid has established itself as a pioneer in autonomous mobile robot (AMR) technology. Supporting more than 50 global brands, Seegrid’s AMRs have transformed manufacturing and distribution facilities by automating up to 80% of non-conveyed material moves and reducing inventory requirements by up to 30%. However, as customer workflows grew increasingly complex, Seegrid’s Application Engineering team recognized the need for more sophisticated tools to design and validate their solutions. This case study explores how Seegrid partnered with Simio to implement discrete event simulation, enabling them to optimize AMR fleet operations, reduce congestion, and deliver more efficient solutions to their customers.

Company Background

Founded in 2003 by Carnegie Mellon graduate Hans Moravec, Seegrid has deep roots in Pittsburgh, Pennsylvania. The company specializes in autonomous mobile robots that automate material handling processes across manufacturing, logistics, and warehousing environments. Seegrid’s product line includes the Lift CR1 (capable of handling loads up to 15 feet high and 4,000 pounds), the Lift RS1 (for low lift applications up to 6 feet and 3,500 pounds), and the Tow Tractor (for long-distance material transport).

What sets Seegrid apart is their infrastructure-free navigation system built on stereo vision and 3D mapping technologies. This innovative approach allows their AMRs to navigate complex environments without requiring physical infrastructure modifications. With a perfect safety record of zero reportable incidents, Seegrid has earned the trust of major manufacturers and distributors worldwide.

“There’s a revolution happening in material handling,” explains Sydney Schooley, Manager of Application Engineering at Seegrid. “The future is autonomous, and at the heart of this revolution is Seegrid supporting more than fifty global brands. We continue to deploy our trusted and proven AMRs in live customer environments, completely transforming the way these facilities operate.”

The Challenge

Limitations of Traditional Planning Methods

For years, Seegrid’s Application Engineering team relied on an Excel-based model to design customer solutions. This tried-and-true method involved creating routes in CAD and then extracting that data to combine with speed profiles using an Excel macro. While effective for basic scenarios, this approach had significant limitations when dealing with complex workflows and interactions between multiple AMRs.

“Knowing that our product has grown more and we are seeing more complex workflows with our customers, we knew we needed a different tool in our toolbelt,” Schooley explains. “Hence discrete event simulation that helps us better understand and visualize AMR interactions at key junctions to reduce congestion.”

Specific Challenges in AMR Fleet Operations

The Application Engineering team faced several critical challenges that their Excel model couldn’t adequately address:

• Visualizing AMR Interactions: Understanding how multiple vehicles would interact at intersections and identifying potential congestion points was difficult with static models.
• Optimizing Job Sequencing: In manufacturing environments, particularly those with single presentation of parts, timing the replacement of empty containers with full ones (known as “hot swapping” or “job sequencing”) required precise coordination between multiple AMRs.
• Handling Peak Volumes: Determining whether the same fleet size could handle both average and peak volumes throughout the year was challenging without dynamic simulation.
• Incorporating Special Behaviors: Accounting for special zones (like slow zones near break rooms or fire door zones) and their impact on fleet performance required more sophisticated modeling.
• Testing “What-If” Scenarios: The team needed a way to experiment with different fleet sizes and configurations to find the optimal solution for each customer’s unique requirements.

“Setting the stage, single presentation of parts is a common workflow we see in manufacturing material handling,” Schooley notes. “Often manufacturing floor space is at a premium… With many parts that need to be added to a product, you often see one container of that material in single presentation of parts.”

This scenario creates a time-critical situation: when a container of parts is depleted, the clock starts ticking for a material handler to exchange the empty container for a full one. With hundreds of parts needed for complex assemblies like automobiles, this creates significant congestion challenges for both human material handlers and automated systems.

The Solution: Simio Discrete Event Simulation

In 2024, Seegrid began searching for a simulation partner to enhance their solution design capabilities. After evaluating various options, they selected Simio for its powerful discrete event simulation capabilities and collaborative approach.

“We went with Simio because we saw that there was going to be a great partnership there,” Schooley explains. “We can’t give enough praise to our developer, Adam from Simio. He created the positive collaborative environment. He was always interested in our product, interested in what we were talking about, made sure to understand what our actual problem was, and we felt really great about this model representing how Seegrid would do this in the real world.”

Key Simulation Capabilities

The Simio simulation model provided Seegrid with several critical capabilities:

• Visualization of AMR Interactions: The simulation allowed the team to see how AMRs would interact at key junctions, identifying potential congestion points before implementation.
• Job Sequencing Modeling: Simio enabled the modeling of complex job dependencies, ensuring that one AMR would complete its task (e.g., removing an empty container) before another AMR would begin its related task (e.g., delivering a full container).
• Traffic Analysis: The simulation provided detailed analytics on intersection wait times, helping identify bottlenecks and optimize routing.
• Experimentation Framework: Simio’s experiment capabilities allowed the team to test different fleet sizes and configurations to find the optimal solution for each customer’s needs.
• Multiple AMR Type Support: The model could simulate different types of AMRs (like the Lift CR1, Lift RS1, and Tow Tractor) operating simultaneously within the same facility.

Implementation Process

The implementation of Simio simulation at Seegrid followed a structured approach:

• Model Development: Seegrid formed a “Tiger team” consisting of Sydney Schooley, Sofia Panagopoulou (Application Systems Engineer), and Abby Perlee (Application Engineer) to collaborate with Adam Sneath from Simio on developing the initial model.
• CAD Integration: One of the early challenges was integrating CAD drawings of facility layouts into the simulation model. The team developed a Python script that extracted route networks from CAD files and converted them into a format that could be imported into Simio.
• Fleet Management Logic Implementation: Seegrid’s fleet management software logic was translated into the Simio model, ensuring that the simulation accurately represented how the AMRs would behave in real-world environments.
• Template Creation: The team created a template model that could be easily adapted for different customer scenarios, streamlining the simulation process for future projects.
• Team Training: Once the initial model was developed, the entire Application Engineering team was trained on using the simulation tool, with a focus on practical application to customer scenarios.

“We formed a Tiger team – myself, Sofia…as well as Abby,” Schooley explains. “We are responsible for collaborating with Simio to help develop the model. And then throughout the process, once we felt the model was in a good spot, our whole team, including myself as nine people, we got everyone else more involved.”

Results and Benefits

The implementation of Simio simulation has delivered significant benefits to Seegrid’s Application Engineering team and their customers:

Enhanced Solution Design

The simulation model has enabled Seegrid to design more efficient and effective AMR solutions for their customers. By visualizing complex interactions and identifying potential bottlenecks before implementation, the team can optimize routes, fleet sizes, and job sequencing to maximize efficiency.

“Does this solution over time see major traffic jams under a section ten? Does this add significant time to our cycle time? How can I reduce this traffic jam?” Schooley asks. “This is more problem solving on the application engineers to see how is this solution being done and how can I help it? How well do we keep up with production volume?”

Improved Customer Communication

The visual nature of the simulation has enhanced Seegrid’s ability to communicate complex solutions to customers. Being able to show a 3D visualization of how the AMRs will operate within the customer’s facility helps build confidence in the proposed solution.

“And that visual is very impactful for my team,” Schooley notes.

Optimized Fleet Sizing

The ability to experiment with different fleet sizes has helped Seegrid optimize the number of AMRs required for each customer application. This ensures that customers invest in the right number of vehicles to meet their needs without over-purchasing.

“We’ll compare how changing volumes affect AMR fleet count,” Schooley explains. “For instance, if the customer has average volumes throughout most of the year, but then they peak at Christmas, can we still use the same vehicles to help them at peak time as well?”

Efficient Job Sequencing

The simulation model has been particularly valuable for optimizing job sequencing in manufacturing environments. By ensuring that empty containers are removed before full containers are delivered, Seegrid can minimize the time that operators are without parts, maintaining production efficiency.

“A process flow of job sequencing in manufacturing might look like an operator calls a container to be replaced through an integrated button press,” Schooley describes. “That signal starts a job. We’ll call it empty for full container for part one two three four. Then the Seegrid Lift AMR number one will pick up the empty container from the assembly line and take that to a location for empty containers. Then AMR two needs to pick up a full container from a staging area and place it in that same position at the line. However, AMR two will not start their job until AMR one has fully removed the empty container from the line.”

Bottleneck Identification and Resolution

The simulation’s ability to track wait times at intersections has helped identify and resolve potential bottlenecks in AMR routing. This has led to more efficient material flow and reduced congestion in customer facilities.

“Bottlenecks can be created in the application, and they cannot be easily identified without specific data,” explains Sofia Panagopoulou, Application Systems Engineer at Seegrid. “Traffic management is configured based on our fleet management software, and then using the Simio dashboards that show the wait times per intersection, we can identify the bottlenecks.”

Implementation Challenges and Solutions

While the implementation of Simio simulation has been highly successful, the team did face several challenges along the way:

Team Education and Training

One of the primary challenges was educating the entire Application Engineering team on the new simulation tool and process. To address this, the team developed comprehensive documentation, including an initial release training program, troubleshooting guides, and feature-specific tutorials.

“As always, it takes time to get used to a new process and work with a new tool,” Pennacchio notes. “Initially, we ask for team feedback during the development, so there was an initial release training that the whole team had to go through and give us feedback to make sure that we have a useful simulation for everyone.”

Translating Fleet Management Logic

Another significant challenge was translating Seegrid’s sophisticated fleet management software logic into the Simio model. This required careful balancing between model accuracy and development time.

“Seegrid Fleet Management is based on years of software development and vehicle capabilities, and it had to be built in the simulation template,” Pennacchio explains. “The key to overcoming this hurdle is to identify the capabilities that are needed. There is a balance between model accuracy and development time because you never want to spend a lot of time to make the simulation identical to the product when you know you are not using all the capabilities of it.”

Keeping the Model Updated

As routes and requirements change between the sales and implementation phases, keeping the simulation model updated can be challenging. The team has developed processes to ensure that changes in CAD drawings are reflected in the simulation model.

“Between the sales and implementation phases, the route might change,” Pennacchio notes. “In simple words, changing the CAD routes means updating the whole simulation.”

Future Outlook

Seegrid sees the partnership with Simio as just the beginning of a new chapter in their solution design capabilities. As their applications continue to grow more complex, the need for sophisticated simulation will only increase.

“Seegrid’s Application Engineering scope and capabilities have expanded with using Simio to model customer workflows,” Pennacchio states. “Our plan is to expand our approach to align with evolving business strategies. That includes adding more features, being able to support more workflows, and adding new products. All of the above lead to more of a need for simulation, as the applications keep getting more and more complex.”

The team plans to continue enhancing the simulation model with additional features and capabilities, further improving their ability to design optimal AMR solutions for their customers. This includes:

• Adding New Product Models: As Seegrid develops new AMR products, these will be incorporated into the simulation model.
• Supporting More Complex Workflows: The team will continue to enhance the model to support increasingly complex customer workflows.
• Expanding Feature Set: Additional features will be added to the simulation model to provide even more detailed analysis and optimization capabilities.

Conclusion

The partnership between Seegrid and Simio demonstrates the power of discrete event simulation in optimizing autonomous mobile robot operations. By implementing Simio’s simulation capabilities, Seegrid has enhanced their ability to design efficient, effective AMR solutions for their customers, addressing complex challenges like job sequencing, traffic management, and fleet sizing.

“To recap, we saw how Seegrid application engineers add a new tool to their toolbelt for solution design and discrete events simulation using Simio,” Schooley concludes. “We also saw how a complex real-world problem in job sequencing or hot swapping is simulated in Simio. And we saw the great start to a new partnership between Seegrid and Simio.”

For manufacturing, warehousing, and logistics operations facing similar challenges with material handling automation, the Seegrid-Simio case study offers valuable insights into how simulation can optimize autonomous mobile robot deployments, reduce congestion, and improve overall operational efficiency.