Simulation of mobile robots (AGVs and AMRs): Why realistic planning is key to project success

Mobile robots are considered a key technology for flexible production and logistics systems. At the same time, projects involving them are among the most demanding automation initiatives. Mobile robot simulation provides the foundation for realistically modeling complex interactions and validating planning decisions at an early stage. In this article, we show where traditional planning methods reach their limits, what challenges make realistic modeling of mobile robot systems difficult, and what role simulation plays in the individual project phases for making informed decisions. You will learn how dynamic effects can be identified early on and why realistic simulation is a decisive success factor for the planning, design, and scaling of mobile robot systems.

Mobile robots – including AGVs and AMRs – are becoming increasingly important in intralogistics. At the same time, the complexity of projects is rising.

Companies are therefore faced with a key question:

How can the actual system behavior of mobile robots be reliably assessed before implementation?

Simulations of mobile robots help to realistically map material flows and identify planning risks early on. They provide a robust basis for decision-making, provided that the simulation accurately reflects real system dynamics.

In the following, we use the term “mobile robots” as an umbrella term for AGVs and AMRs.

Why are more and more companies turning to mobile robots?

Companies use AGVs and AMRs to stabilize material flows. They reduce manual transport and increase process reliability. In addition, mobile robots handle repetitive transport tasks. They relieve employees and can be deployed around the clock.

Another advantage lies in flexibility. Layout changes can be implemented more quickly than with rigid conveyor systems. Furthermore, AGVs and AMRs improve transparency. Vehicle movements and transport times become measurable.

These advantages are driving broad market penetration. The global market for AGVs and AMRs in logistics has been growing at double-digit rates for years. Market studies forecast annual growth rates of around 13 to 17 percent through 2035. In 2024 alone, over 200,000 new AGV and AMR units were installed worldwide. A large share of these is in manufacturing and intralogistics, where mobile robot systems are increasingly seen as the standard solution.

However, as adoption increases, so does complexity. Fleets are becoming larger and more heterogeneous. This is precisely why robust planning is becoming increasingly important. Incorrect assumptions have a direct impact on throughput, costs, and operational reliability.

The larger and more dynamic the systems become, the greater the impact of inadequate or overly superficial planning approaches. They quickly lead to systems being over- or under-dimensioned, directly affecting efficiency and investment costs.

Why do many mobile robot projects fail during the planning phase?

Many projects start with simplified assumptions. Static calculations, such as Excel-based models, are frequently used. However, these approaches do not account for dynamic effects, including vehicle encounters, charging cycles, or fluctuating transport orders.

In practice, this often leads to incorrect designs. Bottlenecks, idle times, or unnecessarily high investments are common outcomes. Experience from real projects shows that static methods cannot reliably predict the performance of complex mobile robot systems.

What is the difference between AGV and AMR?

In practice, the terms AGV and AMR are often used interchangeably. For planning and simulation purposes, however, a clear distinction is helpful – even though both represent mobile robot systems.

AGV – Automated Guided Vehicles

AGV stands for Automated Guided Vehicle. AGVs are driverless transport vehicles operating as part of a centrally controlled system. Tasks, routes, and priorities are managed by a higher‑level control system or fleet manager. The overall system behavior results from interactions between vehicles, control logic, and the surrounding infrastructure.

AMR – Autonomous Mobile Robots

AMR stands for Autonomous Mobile Robot. AMRs typically feature more advanced onboard sensors and computing power. They can perceive their environment and react locally to obstacles, allowing more flexible navigation within the layout. Despite this increased autonomy, AMRs are usually still embedded in overarching fleet coordination and task assignment logic. The system behavior continues to emerge from the interaction between vehicles, control strategies, and the environment.

Why this distinction matters for simulation

Regardless of terminology, one principle applies:

Mobile robots never operate in isolation.

System performance is determined by interactions between vehicles, control logic, processes, charging infrastructure, and other resources. These interactions must be represented accurately to achieve meaningful simulation results.

Why are Excel calculations and static designs insufficient?

Static models simplify complex systems by assuming uniform processes. Excel-based calculations work with average values and ignore temporal interactions.

In practice, these assumptions rarely reflect reality:

• Orders cluster over time
• Vehicles block one another or wait at bottlenecks and charging points
• Throughput does not scale linearly with the number of vehicles
• Transport orders and charging operations are unevenly distributed
• Peak loads arise spontaneously due to order peaks, process changes, or unplanned disruptions

Static approaches are therefore insufficient to capture these dynamics. Planning methods must explicitly represent temporal sequences and interactions to enable reliable assessments of throughput, vehicle demand, and overall system behavior.

What planning tools are available for mobile robot systems and when is each one appropriate?

Various tools can be used for planning and designing mobile robot systems. They differ significantly in terms of analytical depth and the complexity they can represent.

Static planning methods, such as Excel-based sizing, are suitable mainly for very simple scenarios. They provide initial guidance but rely on averages and ignore dynamic interactions.

Some fleet management systems offer integrated simulation functions that model vehicle behavior using the actual system logic. While these simulations provide useful insights, they are usually limited to a single manufacturer’s fleet. Heterogeneous fleets and surrounding processes are often only partially represented or not at all.

Factory simulations take a holistic approach. They model vehicles, processes, and material flows together and embed mobile robots into the context of the entire production or logistics system. This allows heterogeneous fleets with different vehicle types and control strategies to be analyzed realistically. Interactions between vehicles, buffers, processes, and resources become visible, enabling reliable identification of bottlenecks and nonlinear effects.

Virtual commissioning complements this approach by validating control logic and system behavior prior to go-live, allowing misconfigurations and inefficiencies to be identified early.

As system complexity increases, the value of combining planning, analysis, and validation across all project phases grows significantly.

Why a holistic factory simulation of mobile robots makes the difference

Mobile robots are always part of a broader system. They interact with processes, buffers, infrastructure, and people.

Simplified or vehicle-centric simulations typically consider only isolated aspects of the system. External influences and interactions remain unexamined.

Holistic factory simulation, by contrast, connects layout, processes, and material flows in a single model. Bottlenecks become visible, dependencies can be analyzed, and planning decisions become more reliable, especially as fleet size and system complexity increase.

The more complex the system, the greater the added value of holistic simulation.

At the same time, this approach places high demands on data quality, modeling depth, and accuracy.

What challenges make realistic simulation of mobile robots difficult?

Realistic simulation of mobile robots is demanding due to dynamic interactions, system variability, and complex control logic.

Dynamic encounters and bottlenecks

Vehicles influence one another. Encounters reduce speed, cause waiting times, and amplify congestion at bottlenecks. In extreme cases, deadlocks may occur, directly affecting overall system performance.

Charging behavior and energy cycles

Battery state determines vehicle availability. Charging processes temporarily remove vehicles from operation, directly influencing throughput and utilization.

Influence of control strategies

Task prioritization, dispatching rules, and routing logic affect empty runs, transport times, and fleet utilization. A realistic simulation must accurately reflect these control decisions.

These challenges illustrate why simulation should not be applied in isolation but strategically across all project phases.

How do the requirements for simulating AGVs and AMRs change across the project phases?

Projects involving mobile robots typically progress through several phases. Each phase places different requirements on planning tools and the level of model detail.

Concept and proposal phase

Simulation as an orientation and decision‑making tool

In the early project phase, simulation is primarily used to provide orientation and support decision‑making – not to build a fully detailed model. The objective is to make reliable initial statements and avoid planning concepts that are either too conservative or overly optimistic. In particular, the estimated number of vehicles must be realistic in order to calculate economically viable projects and convince stakeholders.

Character of the phase

• Early decision‑making stage
• High uncertainty
• Limited data availability

Typical questions

• How many vehicles are generally required?
• Is a mobile robot system suitable for the planned throughput?
• Where are potential bottlenecks in the layout?
• Is the concept fundamentally cost‑effective?
• How can the system be clearly communicated to decision‑makers?

Role of simulation

• Rough sizing
• Comparison of basic variants
• Validation of fundamental assumptions

Objectives

• Avoid over‑ and under‑dimensioning
• Identify risks at an early stage
• Provide a reliable basis for proposals and investment decisions

Design Phase

Simulation as an analysis and optimization tool

In the design phase, the simulation model becomes more detailed. Layout variants, routing concepts, traffic rules, and control strategies are systematically compared. The focus shifts from basic feasibility to identifying risks, understanding system behavior, and refining assumptions before implementation.

Character of the phase

• Increasing level of detail
• Clearly defined objectives
• Structured comparison of variants

Typical questions

• How do different layout variants affect throughput and waiting times?
• Which control and prioritization strategies are suitable?
• How do vehicles behave during encounters or at bottlenecks?
• Where do congestion and blocking occur?
• How robust is the system under fluctuating demand?

Role of simulation

• Detailed depiction of dynamic effects
• Systematic comparison of design variants
• Evaluation of control and dispatching strategies

Objectives

• Reduce implementation risks
• Achieve a realistic understanding of system behavior
• Make informed decisions prior to deployment

Commissioning

Simulation as a verification and validation tool

Commissioning marks the critical transition from planning to real operation. At this stage, time and cost pressure are particularly high. Simulation is used to validate system behavior and control logic under realistic conditions before go‑live.

Character of the phase

• Transition from planning to reality
• High sensitivity to time and cost

Typical questions

• Does real system behavior match the simulation results?
• How does the actual control or fleet management system interact with vehicles and processes?
• Where does actual behavior deviate from planned assumptions?
• Which parameters and strategies require adjustment?
• How can issues be detected before productive operation?

Role of simulation

• Integration of the real control component (fleet manager) into the simulation model
• Validation of external control logic under realistic conditions
• Alignment of planning assumptions, control logic, and actual system behavior
• Support for fine‑tuning prior to go‑live

Objectives

• Ensure a reliable commissioning process
• Shorten ramp‑up times
• Avoid unexpected issues during production operation

Operation and further development

Simulation as a continuous decision‑support tool

After implementation, operating conditions continue to change. Order structures evolve, layouts are adapted, and systems are expanded. Simulation supports these changes by providing a continuous basis for analysis and decision‑making, even during live operation.

Character of the phase

• Ongoing operation
• Changing boundary conditions

Typical questions

• How does increasing order volume affect system performance?
• Is the existing fleet still sufficient?
• Where are new bottlenecks emerging?
• Which expansions or adjustments make sense?
• How can KPIs be sustainably improved?

Role of simulation

• Scenario analysis
• Support for system extensions and scaling
• Continuous optimization

Objectives

• Maintain long‑term system performance
• Safeguard investments
• Enable data‑driven system development

A simulation model should therefore be capable of supporting multiple project phases over the entire lifecycle of a mobile robot system.

How Visual Components and the Mobile Robots Add-on support you

The Visual Components simulation platform provides a powerful foundation for clear and efficient mobile robot simulation. It enables flexible modeling of vehicles, routes, and transport tasks within realistic production and logistics environments.

The Mobile Robots library was developed to represent dynamic factors and complex fleets even more realistically. The Mobile Robots Add‑on specifically extends Visual Components with capabilities such as battery management, traffic rules, fleet optimization, and reporting.

This allows not only individual vehicles or routes to be evaluated, but also the real system behavior of mobile robot systems to be analyzed under varying conditions. The effects of peak loads, malfunctions, rule sets, or fleet strategies become visible at an early stage. Planners can compare variants, verify assumptions, and identify risks already in the planning phase.

The Mobile Robots Add‑on for Visual Components therefore creates a robust basis for decision‑making for planning, design, and scaling of mobile robot systems – and makes a decisive contribution to project success even before the first vehicles are deployed in the real system.

Which metrics are crucial for informed decisions?

Simulations provide more than visualizations. They generate measurable KPIs such as throughput, vehicle utilization, wait times, charging events, and bottleneck indicators.

Visual tools like heat maps reveal congestion points and delays. Variants can be compared systematically, and the impact of different layouts or control strategies becomes transparent.

These KPIs can also be reused for internal and external communication, enabling stakeholders to understand system performance and optimization potential based on data rather than intuition.

The DUALIS Statistics Module compiles simulation data on mobile robot systems from Visual Components into structured reports and helps teams analyze system performance, compare layout variants, and communicate results more effectively.

Conclusion: Simulation as the key to cost-effective mobile robot concepts

The use of AGVs and AMRs continues to grow – along with system complexity. Simplified planning approaches are no longer sufficient. Realistic simulation creates transparency by combining planning, evaluation, and optimization. It reduces risk, supports confident decision-making, and lays the foundation for economically successful intralogistics projects.

Would you like to plan and validate AGV/AMR projects with confidence?
Learn how Visual Components and the DUALIS Mobile Robots Library support simulation, analysis, and commissioning and request a live demo.

Would you like to learn more about the DUALIS Mobile Robots Library?
The add‑on page provides an overview of key features and use cases for simulating AGVs and AMRs using Visual Components and the Mobile Robots Add-on.

Sources and further reading:

Dilefeld, M.: Herausforderungen für die Projektierung von Fahrerlosen Transportsystemen (FTS) und Autonomen Mobilen Robotern (AMR). In: Simulation in Produktion und Logistik 2023, Universitätsverlag Ilmenau. DOI: 10.22032/dbt.57476. (Foundation for planning phases, simulation requirements, and methodological classification)

Ullrich, G.; Albrecht, T.: Fahrerlose Transportsysteme – Eine Fibel zur Technik und Planung.
Springer Vieweg, Wiesbaden, 2019. (Fundamentals of AGV system architectures, control concepts, and planning methods –German-language reference – German-language publication)

Business Research Insights: AGV and AMR in Logistics Market – Size, Share and Growth Forecast.
Prognosezeitraum bis 2035. (Market growth and CAGR data)

Market Growth Reports: AGV or AMR Market Report – Global Installations and Adoption.
(Installation figures and adoption of mobile robot systems worldwide)

Ullrich, G.: So werden FTS‑Projekte erfolgreich. Ingenieur.de, 2021. (Practical insights into typical planning pitfalls and success factors – German-language article)

Note: This article was produced in close collaboration with our expert in the simulation of mobile robot systems. The content is primarily drawn from his scientific papers and has been editorially refined with the help of AI. The statements made in this article are based on scientific publications as well as current market and industry studies.