A Digital Twin for robots is a virtual model that replicates a real robot in real time. The digital twin includes both real-world data about the robot, such as current readings (position, current, temperature), faults, etc., as well as all of the robot’s design information (kinematic equations, CAD files, payload limitations). Therefore, users have real-time visibility into the robot’s activities and its performance against its design specifications.
In contrast to a single simulation run, which is static and does not account for changing conditions, a Digital Twin for Robots will continuously update and reflect the changing conditions and environment around the robot, such as new tooling, part wear, different parts, changes to the robot program, etc.
The use of a Digital Twin for Robots enables teams in manufacturing and warehouse settings to simulate robot paths, cycle times, and safety zones before making physical changes to the robot’s cell. For example, engineers may want to test alternative layouts, verify reach and collision points, and confirm handoffs with other equipment, such as conveyors or vision systems. By simulating these events, teams can reduce downtime by making fewer changes on the shop floor. Since every minute counts on the shop floor, reducing downtime is highly valuable.
It also helps improve performance post-deployment. A digital twin for robots will allow you to compare your expected behavior to the actual behavior of your robot, which will help you identify if there is a ‘drift’ in your system; this includes longer motion times, increased torque, increased vibration, or repeating ‘near-miss’ collisions.
This will enable you to perform faster root-cause analysis and support predictive maintenance of your components (servicing components based on their condition rather than a predetermined calendar). Over time, you can refine your speeds, acceleration, and tool paths to meet your desired quality standards while decreasing your energy usage and mechanical stress.
The typical steps to create a digital twin for robots include: (1) a high-quality model of the robot; (2) an interface to ingest controller and sensor data from the robot; (3) a physics or motion engine to replicate the motion of the robot; and (4) dashboards and alert capabilities to provide the operator. The most significant barriers to creating digital twins for robots are: (1) the quality of the data provided by the sensors and controllers; (2) the ability to integrate data from various vendors; and (3) maintaining the alignment of the virtual model to the changes made in the physical robot (i.e., swapping tools or updating firmware).
As robots become increasingly interconnected, the digital twin has evolved into a viable solution for reducing automation-related risks, accelerating commissioning, and sustaining system reliability, turning data collected from robots into operational decisions that reduce downtime and increase productivity.
If you’ve ever followed along as your car’s icon moved around the map in Google Maps, then you’ve witnessed a simple example of a digital twin. A digital twin is a digital “you” moving through a digital environment, receiving real-time updates as you move.
If we apply this concept to a large, expensive factory robot, we could provide similar live-tracking capability, with a near-perfect digital copy of the robot that allows engineers to identify potential issues before they occur and test new job functions safely in a virtual environment. This concept of creating a digital twin of a robot represents a powerful application of robotics technology and will continue to be a growth area in the coming years.
In addition to a static 3-D model of the robot, a live connection to the physical robot provides much more. The live connection is a “digital nervous system” created by numerous sensor inputs from the actual robot, providing continuous information regarding the robot’s location, temperature, and speed to its digital copy (twin). As a result, the original digital representation is transformed from a static graphic into a dynamic digital twin that reflects the robot’s status and all its actions/conditions in real time. The digital twin does not record the robot’s movements or conditions; it is a real-time duplicate.
This explanation for what a robotic digital twin is does not need an engineering degree because this is a concept based on common sense and knowledge. This article explains how these robotic digital twins are creating new opportunities worldwide for robots—one robot at a time—using five easy steps.
Understanding the Robot’s “Digital Shadow”
“A Digital Twin” no longer represents an abstract icon on a map. Rather, the concept has evolved into a robust industrial tool: a fully functional, continuously evolving digital model (copy) of a robotic device that will learn, practice, and predict potential problems before they occur. A digital twin is not simply a digital image of a robot. Instead, it represents a fully operational model connected to actual data. This enables robots to be both safer and more intelligent, ultimately creating a safer, more intelligent environment for humans.
When you hear the term “Digital Twin” again, you’ll have the confidence to provide a detailed explanation of its value. “Digital Twins” are not merely clever smart manufacturing technologies. They offer a glimpse into the potential of robotics. By creating a virtually perfected robotic model, we develop a much better physical model of the products and systems we rely on in our daily lives.
Step 1: Meet the Two Players – The Robot and Its Digital Ghost
Let us visualize the physical actor in this story: the industrial robot. Take away all the walking, talking characters from movies, and instead, consider an arm in a warehouse that can pick up very heavy boxes or a welding machine in a factory that welds automobile doors. The industrial robot is a magnificent example of engineering, as it is programmed to execute a series of predetermined actions with absolute precision. The robot performs its assigned duties repeatedly but only does what its programming instructs it to do.
Next, we will create a 3D digital model of the physical robot in a computer program. The model of the robot will look exactly the same as the physical robot (down to the smallest detail, such as every single bolt). The digital model will also appear to move around on the screen, as a character would in a high-quality video game.
For a brief time, it is simply a very nice-looking digital puppet, which is completely silent and detached from the physical world. This is the beginning of creating a “digital twin” concept; it still lacks the most critical component.
When we establish a real-time communication link between the two, the magic begins. Just as your cell phone’s GPS sends information about your location, and therefore causes your GPS symbol to move on a map in real-time, a variety of sensors on the physical robot send real-time data to the digital model of the robot (such as its current location, its velocity, and its condition), and turn the digital model into a dynamic representation of the physical robot.
The digital model becomes a “live” representation of the physical robot, or a “digital shadow,” which accurately represents the condition of the physical robot at all times. It is no longer just a digital model; it is now a living dashboard for the physical robot.
Digital Twin Technology: Virtual Models That Mirror Real Robotic Systems
Digital Twin Technology: A virtual replica (model) of a real robotic system
Digital twin technology produces a virtual model of an object in the physical world that remains linked to it via information exchange. This means that, in robotics, a digital twin of a robot could capture its geometry, motion limits, tools, and workcell layout.
A digital twin for robots will also update automatically as live data streams into it from the robot, such as position, torque, temperature, and alarm status. The end result is a model that not only looks like a robot, but it acts like one at the moment.
One of the major benefits of using digital twin technology is the ability to run faster, safer tests. Engineers can test paths, check for collisions, determine cycle times, and validate reachability before running their production code. Digital twin technology also allows engineers to do “what-if” testing. For example, they can simulate a different gripper, adjust speed, change a fixture’s location, or add a camera without shutting down production. If a digital twin for robots is used to support commissioning, integration issues can be identified earlier, reducing costly rework.
Once deployed, digital twin technology can improve a robot’s reliability and performance. A digital twin for robots can evaluate how a robot’s motion and load deviate from their expected values based on actual measurements and alert engineers to any deviations or trends, such as drift, wear, or misalignment. If torque levels rise on a particular joint or a path takes longer than expected, the twin can detect these changes and direct the engineer to corrective actions.
Digital twin technology can also enable predictive maintenance by monitoring equipment condition over time, allowing the service provider to address any degradation indicated by the data rather than following a predetermined schedule.
For a Digital Twin to function effectively, it requires accurate models, reliable data pipelines, and regular updates of all elements affected by changes to tooling, payload, calibration, firmware, or cell layout. The effectiveness of Digital Twin technology increases when it is integrated with robotic control systems, sensors, and production equipment, and when users view the Digital Twin as an ongoing operational element rather than a single project.
In the future, Digital Twin technology will enable fleets of robots to manage themselves, coordinating multiple robots simultaneously, simulating throughput, and assigning tasks within a location. As Digital Twin technology becomes more widely adopted, Digital Twin for Robots will provide a practical foundation for the rapid rollout of automation, increased uptime, and greater predictability in quality—converting robot data into actionable information that both operators and engineers can trust.
Step 2: The Virtual Playground – Why Give a Robot a Twin?
So, why go to all the trouble of creating a digital ghost for a physical machine? The answer is simple: risk. A real-world industrial robot is incredibly expensive, and programming it to do a new task is complex. A small coding error could cause a collision, damage the robot, or shut down an entire production line, costing a fortune. A digital twin gives engineers a priceless “do-over” button.
This is where simulation comes into play. Think of it as a video game or a flight simulator, but for the robot. Inside this safe, virtual environment, engineers can test new ideas and push the robot to its limits without any real-world consequences. They can ask, “What happens if we make the arm move 20% faster?” and see the results instantly on the screen. This digital practice field is essential to improving a robot’s performance without risking a single scratch on the actual machine.
Beyond just practicing, the twin allows for something called offline programming—or “teaching the twin first.” Imagine the real robot is busy on the factory floor, assembling products. Meanwhile, an engineer can be in an office, training the digital twin to perform a completely new task. Once the new program is perfected in simulation, it can be deployed to the real robot, enabling task switching with minimal downtime. Production doesn’t have to stop just to teach the robot a new trick.
Ultimately, this virtual playground is about working smarter, not just harder. It saves companies significant time and money, prevents accidents, and enables them to optimize their robots’ operation. But for this digital practice to be truly effective, the twin can’t just be a good guess—it has to be a perfect mirror.
Digital Twin Benefits: Reduce Risk, Cost, and Development Time
Virtual digital twins are digital representations of real-world systems and enable teams to test design concepts and other ideas before making changes to physical systems. One of the first Digital Twin benefits to automation teams will be increased visibility into how a robot (or robots), the tooling associated with the robot(s), and the entire cell environment, should perform, versus how they are performing in real time. A well-developed Digital Twin for Robots has become an effective “sandbox” for teams to use when making decisions.
Risk mitigation is one of the most significant benefits of Digital Twin across industries. Teams can develop validated paths, safety zones, and handoff points in the digital model rather than on the plant floor, where they may discover too late that a collision, reach limit, or unstable motion exists. Using a Digital Twin for Robots, teams can test hundreds or thousands of scenarios (e.g., part position, payload, speed) without putting people, equipment, or production at risk.
Cost containment is another major benefit of the Digital Twin. Physical prototyping, rework, and longer-than-necessary commissioning times are costly. Designing, testing, and validating design concepts such as layouts, grippers, and cycle times in a digital format eliminates the need for last-minute changes, saving significant money. Additionally, using a Digital Twin for Robots enables teams to program robots offline and facilitates smooth task transitions, reducing downtime and enabling continuous improvement with minimal disruption.
The development speed is where many of the Digital Twin Benefits compound. The teams may be able to iterate on their software development much more quickly, since the virtual testing environment allows them to test software changes, collaborate across all site locations (virtually), and document their work in a reproducible way.
When first entering operational mode, a Digital Twin for Robots can identify “bottleneck” issues (e.g., robots sitting idle for too long, poor robot sequencing), so these issues are addressed before customer complaints about delays.
Operational Reliability builds on additional Digital Twin Benefits once the Digital Twin is in operational mode. Since the twin will have access to both expected vs. actual performance data, it will be able to expose the “drift”, “wear”, or “miscalibration” in a process, which can lead to predictive maintenance as well as trending and identifying potential root causes prior to equipment failure.
In summary, the best Digital Twin Benefits will result from developing a more predictable robotic system (fewer surprises, fewer costly corrections, and shorter development cycle from conceptualization to stable manufacturing).
Robot Simulation Software: Test Robotic Behavior Before Real Deployment
Robot simulation software (robot simulation) enables a team to simulate a robot and its work cell within an online environment prior to running any part on the shop floor. Robot simulation allows validation of reach, payload, cycle time, collision risk, and safety zone requirements, while providing opportunities to experiment with tooling and layout options. Robot simulation software reduces surprises during commissioning and enables projects to move from design to production with fewer delays.
One major benefit of using robot simulation software is that engineers can program their robot’s motion offline, refine it, and transfer it to the robot controller with much less trial-and-error. In addition to enabling motion validation and experimentation, robot simulation software also validates how all components of the robot’s work cell interact. The robot simulation software can be used to verify the interaction among the fixture, conveyor system, sensor, and guard rail as designed.
Using simulation software alongside real-world data enables the simulation to evolve into a digital twin for robots. The digital twin connects the virtual model to live signal sources, including joint state, alarm conditions, and process metrics.
For integrators, robot simulation software enables “what-if” simulations. For example, the user could simulate the effects of changing a gripper, adjusting the approach angle, modifying the robot speed, or adding a visual inspection step.
If the simulation indicates a bottleneck or collision condition, the user can correct these issues in the simulation before physically altering the equipment. This capability is particularly beneficial for integrators with limited time to complete a project or facing high downtime costs. The digital twin for robots extends this value proposition by keeping the model up to date as tools degrade, payloads change, or the program evolves.
“Robot simulation software can estimate production capacity during ramp-up phases to distribute the task load evenly amongst multiple robots. In addition, Robot Simulation Software can test handoffs and timing requirements for robotic collaboration and high-speed pick-and-place applications. By feeding actual machine performance data back into the model, a Digital Twin of a robot can identify deviations from anticipated cycle times and increasing torque values that could indicate mechanical problems.
During operation, Robot Simulation Software can support continuous improvements to the robotics system. The team can simulate upgrades, new product types, and layout configurations using the digital twin without running trials or producing scrap parts. The robot’s Digital Twin will support the team by comparing “planned” vs. “actual” states to provide an accurate comparison.
The most effective use of Robot Simulation Software and Digital Twin for Robotics requires the digital model to remain up-to-date and accurate (payload, calibration, tool center point) and includes realistic limitations (speeds, accelerations, singularities). Any updates made to the physical environment should be reflected in the digital environment as well. If utilized effectively, Robot Simulation Software and Digital Twin for Robotics will enable teams to implement automation faster, safer, and with better reliability.”
Step 3: The Magic Connection – How the Twin Knows What the Robot is Doing
The sensor network ties the physical robot to its digital replica. These are similar to the robots’ nervous system. A wearable device, such as a step counter and heart monitor, tracks the wearer’s progress (i.e., their steps, their heart rate), while the real robot has numerous sensors that track a wide range of factors (position, speed, temperature, etc.) for every motion, regardless of how slight. Every action of the robot produces data that gives it “senses” and enables it to provide feedback on its experiences.
Data is transmitted to the digital twin continuously rather than in a daily report. The image of a vehicle traveling on a map of the area displayed on a cell phone is a useful analogy for how the real robot and its digital twin interact. The image on the phone moves in sync with the real vehicle’s motion.
The relationship between the robot and its digital twin functions in the same manner. The constant exchange of real-time data enables the digital twin to be a current, minute-by-minute representation of the real robot, not merely a dated diagram. Real-time data transmission is what makes the digital twin “come alive”.
Communication between the robot and its digital twin is bidirectional. The robot sends status data to the digital twin, and engineers can send perfect programs from the digital twin to the robot. Following successful testing of a task in the digital twin’s simulated environment, the engineer can be confident in deploying the refined program to the physical robot.
The ability to receive input from the robot and send output to the robot is what gives the digital twin its capability. Once two-way communication is established, the digital twin can do more than simply reflect the robot’s current state; it can begin to forecast future conditions.
Robot Digital Twin: A Live Digital Replica of a Robot
A Digital Twin for robots is a dynamic digital representation of a physical robot that mirrors its hardware, motion, and operational state. It differs from a 2D or 3D representation of a robot because it remains current via real-time data feeds from the robot’s control systems and sensors (position, speed, torque, temperature, fault codes, etc., as well as visual or force feedback).
Since it is an active representation of the robot’s actual state at all times, it provides a reliable reference for teams looking to understand their robot’s behavior, identify faults, or plan changes with fewer trial-and-error iterations.
A Robot Digital Twin is a valuable resource for engineers and commissioning personnel, enabling them to validate the robot’s ability to reach specific areas, avoid collisions, and meet timing requirements before deployment to production. With a Digital Twin for Robots, design teams can test potential paths, modify tool center point locations, check for singularities, and verify the safety zone in a controlled testing environment.
Early use of a Digital Twin for Robots will reduce the need for costly late-stage redesigns, as many layout problems and integration challenges will be identified and corrected before the equipment is installed. Additionally, the Digital Twin for Robots enables teams to create and prepare offline programs that can be updated while the physical cell is still in operation.
In Operations, a Robot Digital Twin is a digital representation of the robot that tracks performance and enables continuous improvement. The Robot Digital Twin enables comparisons of expected and actual cycle times and loadings to identify potential drift due to wear, payload changes, or process variation.
Additionally, a Digital Twin for Robots can support predictive maintenance by monitoring trends (such as increasing torque, vibration levels, or thermal signatures) that typically precede mechanical failure, allowing teams to schedule proactive service rather than reactive downtime.
A Robot Digital Twin must be updated regularly, just like any other production asset, in order to remain valid. Calibration updates, tool changes, payload definitions, firmware updates, and cell modifications should be entered into the Digital Twin in a timely manner. With this level of discipline, the Robot Digital Twin will become a “living” single source of truth for cross-functional teams, including engineering (for testing new ideas), operations (for ensuring stable performance), and maintenance (for taking early action).
Ultimately, the Robot Digital Twin can also provide fleet-level insights over time by establishing a common method for measuring both performance and health across all robots, lines, and sites.
Step 4: Predicting the Future – Two Superpowers of a Robot’s Twin
With a “live” two-way connection established, the digital twin is now much more than a mirror. It provides two powerful capabilities akin to “superpowers,” enabling engineers to predict failures and optimize operations without risking real-world damage to the physical system. The predictive and optimization capabilities of the digital twin enable an existing reactive factory, which is always fixing issues as they occur, to transform into a proactive factory that prevents them from occurring.
1. “The Fortunate Tellers” (Predicting Problems): Since the twin simulates the same stresses experienced by the actual robotic device, it can indicate early stages of wear and tear that may otherwise remain undetected by the naked eye. Essentially, the digital twin serves as a doctor to the robot, indicating that a particular joint will soon fail if not properly maintained. Engineers can schedule preventive maintenance at a time that is most convenient for their operations, reducing the high cost of unplanned downtime caused by equipment failure on the factory floor.
2. “The Dress Rehearsals” (Perfecting New Jobs): If you wanted to train a robot to perform a complex assembly job, such as a new smartphone model, instead of using the expensive trial-and-error method with the physical robot, you would use the digital twin to simulate and rehearse the entire process. The digital twin enables the engineer to fully rehearse each step, eliminate bugs, and refine movements in the virtual world; thus, when the engineer sends the final version of the program to the physical robot, it will execute correctly from the outset.
The ultimate result of the ability to predictively and optimally manage a manufacturing operation is to create a safer, smarter, and more reliable environment. The resulting operational efficiency creates a ripple effect that ultimately benefits everyone.
Robotics Digital Twin: Smarter Robotics Through Continuous Virtual Feedback
A robotics digital twin is an up-to-date virtual model of the robot and its surroundings. The model will include correct geometrical models, as well as correct kinematic models of the robot’s tool, payload, and cell layout. Additionally, it includes the correct data from all controller and sensor inputs to the robot.
What makes a robotics digital twin so useful is that it receives continuous updates from the physical robot, enabling better decision-making before issues lead to lost production time. In practice, a digital twin for robots ingests data from the robot and transforms it into actionable knowledge, rather than just log entries.
In addition to reducing testing time, a robotics digital twin will assist engineering teams in testing reach, potential collisions, safety zones, and cycle-time requirements during the design and commissioning phases without disrupting production. Engineering teams will be able to simulate paths, refine the sequence of operations, and verify how each operation will be completed by another robot, conveyor, or vision system. With the inclusion of a digital twin in the workflow, a digital twin for robots will significantly reduce commissioning time by enabling virtual identification and correction of errors.
The real benefit of a Robotics Digital Twin lies in continuous virtual feedback post-deployment. A Robotics Digital Twin will enable comparison of predicted motion profiles and load behaviors with actual behaviors (e.g., torque, cycle time) to identify performance drift (e.g., increased torque, longer cycle times).
Performance drift may be due to tool wear, calibration issues, suboptimal part presentation, mechanical failure, or other causes. With a Digital Twin for Robots, teams can troubleshoot much more quickly by replicating problem scenarios in the digital twin, narrowing down which variable(s) are causing the issue, and testing potential solutions before making changes on the shop floor.
A Robotics Digital Twin also provides a basis for predictive maintenance. Rather than performing scheduled service on the robots, maintenance can act on trending data indicating the robots are under elevated stress or exhibiting abnormal behavior. This is especially beneficial in high-mix environments, where payloads and trajectories are constantly changing. In addition to providing support for predictive maintenance, a Digital Twin for Robots can provide management for updates, i.e., new programs, new parts, new tooling, by testing those updates virtually and documenting what impact they would have on quality and throughput.
To ensure the accuracy and trustworthiness of a Robotics Digital Twin, organizations must consistently enter accurate data, including calibration, tool center points, payload definitions, firmware version numbers, and any updates to the robot’s layout. As long as this discipline exists, a Digital Twin for Robots can become a common reference point for engineering, operations, and maintenance personnel to improve collaboration and make robotics smarter through continuous, measurable feedback.
Step 5: From the Factory to Your Front Door – How Robot Twins Affect You
While a “smarter” and “more efficient” manufacturing facility may seem distant from the consumer who buys its products, it has many ties to the customer. When a logistics center uses robots (with digital twins) to sort its packages, the number of errors decreases significantly. Therefore, increased speed and reduced error rates at the logistics center result in consumers receiving their online orders faster and with the correct items. As such, consumers experience greater reliability in both holiday purchases and weekly grocery shopping.
Additionally, the new technology being used in the logistics center is developing ways for humans and machines to work more safely as teammates. The “Collaborative Robot,” or “Cobot,” is a machine that works directly with a human worker. It is necessary to create a digital twin for collaborative robots to enable safe simulation while working alongside humans.
The potential is vast, but creating these advanced digital twins is not yet a common practice. The major obstacles to using digital twins today are the cost of creating a robot digital twin and the complexity of system setup. However, as technology becomes easier to use and less expensive, these “invisible helpers” will appear more frequently in our daily lives and continue to improve our surroundings.
Conclusion:
Robotics can be transformed from “build, deploy, and hope” to a repeatable, data-driven process by creating digital twins. By using these 5 steps (1) define the use case, (2) develop an accurate model, (3) connect real data, (4) validate the accuracy of the model through realistic simulation scenarios, and (5) continue to optimize using the digital twin, you will create a functional systems that support both engineering and operational needs.
In most cases, the greatest benefits will occur rapidly: safer testing prior to commissioning, fewer robotic collisions and unanticipated events, faster changeover times, and improved ability to identify the root cause of performance changes over time. Additionally, over time, the digital twin will become a more valuable asset by capturing the behavior of the robot in your specific environment, including the types of parts used, tooling, schedules, and constraints, allowing for optimization that is evidence-based rather than guessing.
Discipline is essential. As much as you would update a living thing, the reliability of a digital twin is dependent upon the quality and frequency of its inputs and updates. Therefore, it should be treated as a living asset. Keep all information regarding calibration, payloads, tooling, and layout current, track data quality, and document all changes made to the digital twin. When implemented properly, a Digital Twin for Robots will become a common reference point that helps reduce risk, lower costs, and shorten the development cycle, while enabling your robots to operate more consistently every day.
Q & A:
- Q. What Is A Digital Twin For Robots?
A. Digital twins allow you to have a virtual replica of a real robot and its work cell, which connects your robot’s motion, the physical space around the robot, and all other operating information, so you can perform testing, monitoring, and make improvements to your robot’s performance in a safe environment. - Q.How Does The Digital Twin Differ From Normal Robot Simulation?
A. Normal simulation typically happens on a computer in a static environment, outside of production. A digital twin will be fed live signals, such as joint positions, alarm status, cycle time, payload, etc., to provide a true representation of the system’s state in real time. - Q. What Are The Five Key Steps To Create A Digital Twin For Robots?
A. (1) Identify what you want to achieve and how you will measure success (KPI), (2) create a precise model of the robot and workcell, (3) feed the model with real-time data, (4) test the model by simulating real-world events, (5) continuously use the model to monitor and optimize performance. - Q. Which Benefits Will Teams Get First Using Digital Twins?
A. Faster commissioning, reduced frequency of collision and rework, improved cycle-time accuracy, safer testing of new configurations, and faster troubleshooting of degraded performance. - Q. What Typically Causes Digital Twin Projects To Fail?
A. Poor model accuracy, poor data quality, lack of integration into controllers/sensors, and failure to update the digital twin when there are changes to the tool(s), payload, layout, or software.
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