There is no doubt robotics professors and researchers often have deep theoretical knowledge in areas like algorithms, control systems, machine learning, and AI. However, the practical aspects of setting up and running a robotics lab can be complex and different from theoretical knowledge. This gap between theory and practice is essential to address.

Establishing a robotics lab involves hardware selection, integrating different systems, ensuring compatibility, and troubleshooting real-world issues. Therefore, collaborating with industry experts and practitioners with practical experience deploying robotics technologies is crucial. Their insights can help select the right tools, equipment, and methodologies that align with research objectives while ensuring practical feasibility. Combining theoretical knowledge with practical expertise can lead to a well-rounded robotics lab that drives innovative research and delivers tangible results.

Objectives

We have had the privilege of engaging with professors around the world who are establishing a robotics lab, and there are generally two primary motivations.

For education: Professors have been assigned a course or the task of creating a course around robotics.

For research: Professors may have specific research interests or project needs that require the establishment of a robotics lab. These labs can serve as vital hubs for innovation and discovery, offering the tools and environment necessary for cutting-edge research.

Equipment

Consider the equipment and technologies that will be essential for your lab. This can include robotic arms, sensors, controllers, and software platforms. Choosing versatile and scalable equipment that can grow alongside your lab’s needs and capabilities is essential.

Securing Funding

Funding is another critical factor. Given the substantial costs associated with setting up a robotics lab, securing grants and funding from various sources is often necessary. It frequently requires building a compelling case for potential funders by highlighting the educational and research benefits your lab will provide.

At Quanser, we strive to be your partner and provide grant proposal service, which saves time and improves your productivity and success rate when it comes to funding.

Collaborations and Partnerships

Collaboration is key. Partnering with other players and thinkers in the industry, other academic institutions, and even government bodies can offer additional resources and expertise. These partnerships can also create opportunities for student internships, collaborative research projects, and enhanced learning experiences.

Developing a Curriculum (If Teaching)

Finally, for teaching labs, focus on curriculum development. Designing courses that effectively integrate the use of your robotics lab will maximize its impact. Ensure your curriculum is aligned with current industry trends and technological advancements to provide students with relevant and up-to-date knowledge.

By taking these steps, you can establish a robust and dynamic robotics lab supporting educational and research goals.

What Should a “Robotics” Include?

Robotics traditionally involved manipulator robotics, like robotic arms used in manufacturing for tasks like assembly and inspection. However, the concept of mobile robots has emerged, including autonomous robots for warehousing and outdoor exploration.

A comprehensive robotics lab should encompass both manipulators and mobile robots to handle a wide range of applications. This broader definition of robotics includes self-driving cars, drones, and any system that senses the world, makes decisions, and acts on those decisions.

Robotics Labs for Teaching

An undergrad or post-grad robotics lab provides students with hands-on experience, fostering critical thinking, problem-solving skills, and creativity. It is a dynamic space where theoretical knowledge meets practical application, enabling students to work on real-world projects, conduct research, and develop prototypes.

The Size of Your Class

For a teaching lab, you need to start by considering your class size. How many lab sessions will you have? How many stations will you need in the lab to ensure that all your students, typically working in groups of two, have a good experience?

Supplementing with a Virtual Lab

You can extend the learning experience virtually using a digital lab like Quanser’s digital twin if you have a large cohort. This is especially relevant when talking about undergraduate labs. Virtual labs allow for a unique approach where, if push comes to shove, you can remotely deploy the same lab experience.

Can You Have a Fully-Digital Robotics Lab?

Our CEO, Paul Gilbert, has been known to respond with the rhetorical question, “Would you ever fly in an airplane that has only been simulated?”

Quanser was built on the foundation that in-person experiences have irreplaceable value. Nothing compares to hearing the robot move, seeing its size in person, trying to push it with your hand, and observing all the sensors. The best way to start is with a physical experience – get your hands dirty.

A great example of bringing theory to life incorporating virtual labs: SDCS mix reality demo

Ensuring Consistency of Your Robots

Consistency is vital for any robotics lab. After all, how can you teach something that doesn’t work in reliable and repeatable ways? One of the challenges professors face is getting their machines to behave consistently, especially when dealing with more than one robot. In our experience, professors frequently find that the second robot they acquire performs differently than the first.

This inconsistency stems from the fact that the design priority for most robots on the market isn’t focused on educational use cases. Instead, the goal is to create cost-effective robots to perform specific tasks.

The best way to overcome this issue and ensure consistency is to purchase all your robots from the same reliable source. Quanser robots are purposely designed and built with educational use in mind, allowing professors to teach concepts effectively.

Deciding the Curriculum

One of the elements that uniquely positions us at Quanser is our comprehensive curriculum. With our mobile robotics lab, we offer a 70-hour curriculum that ensures students gain a deep understanding of the subject matter, even if they are not physically present. This curriculum is meticulously designed to provide an enriching educational experience.

Robotics Labs for Research

For research purposes, it’s important to be cutting-edge and state-of-the-art. Researchers often approach us with very specific and innovative needs. For example, they might be interested in exploring how heterogeneous vehicles can operate within a shared space. This involves ensuring that different types of robots and vehicles can communicate and collaborate effectively without colliding or causing disruptions.

Another common area of interest is the interaction between humans and robots in a factory environment. Safety is paramount in such settings, so researchers are keen on making human-robot interaction as seamless and risk-free as possible. This includes developing advanced sensor systems and robust algorithms that allow robots to predict and respond to human actions in real-time.

Additionally, building a self-driving car is a complex and multi-disciplinary research endeavor. It involves not just robotics but also artificial intelligence, computer vision, and systems engineering. Setting up a lab for this type of research requires various specialized equipment, from high-resolution cameras and LIDAR systems to powerful computational resources for processing the vast amounts of data these systems generate.

In essence, a robotics lab designed for research must be versatile and equipped to handle a wide range of cutting-edge projects.

QArm and its virtual twin in use from McMaster University

Open Architecture

Open architecture is crucial for fostering innovation, collaboration, and adaptability. It reduces dependency on proprietary systems, lowering costs and avoiding vendor lock-in. By embracing an open architecture, a robotics lab can remain versatile, future-proof, and at the forefront of cutting-edge research and development.

At Quanser, all our robots are designed with open architecture. There are no black boxes anywhere, ensuring complete transparency and customization capabilities.

This level of control emphasizes the importance of your programming. It gives users complete authority over the robot’s behavior, enabling a more hands-on and in-depth educational experience in your robotics lab.

On-Board Instruments and Sensors

It’s important to consider the specific types of sensors you want to incorporate. Some people prefer a camera-focused approach, while others might want to include a variety of sensors such as lidar, sonar, microphones, and V2V communication.

At Quanser, we make all these options available, ensuring our platform is sensor-rich and overly instrumented with open architecture. These elements are crucial because they make our systems very attractive to researchers.

Mathematical Soundness

Moreover, all Quanser systems are mathematically sound. Each one comes with a mathematical model behind it, which is a significant feature of both our teaching and research products. Essentially, if the math isn’t crucial to your project, then a Quanser solution might not be the right fit for you. However, for those who need robust mathematical backing for their robotics work, our solutions are tailored to meet these rigorous demands.

Software Agnosticism

Software agnosticism refers to the ability to work with and integrate various software solutions seamlessly, regardless of the underlying platform or technology. This approach ensures that the lab is not tied to a single software ecosystem, providing the flexibility to choose the best tools and applications for each specific task. Additionally, software agnosticism mitigates the risks associated with software obsolescence and vendor lock-in, ensuring the lab can adapt to evolving technologies and market trends.

Our visibility stems from being software agnostic, allowing you to conduct research using your preferred tools, whether it’s Matlab, Simulink, ROS, Python, C, or other platforms that are familiar and expected by robotic researchers.

Planning for Multi-Agent Systems (Heterogenous Vehicles)

Planning a heterogeneous vehicle robotics lab involves:

• Diverse Hardware Integration: Supporting various vehicles with scalable and modular design.
• Robust Communication Protocols: Ensuring seamless interaction among different vehicle types.
• Comprehensive Testing Environments: Simulating real-world conditions for robust testing.
• Safety Standards and Training: Ensuring safety protocols and proper training for researchers and technicians.
• Efficient Data Management and Analysis: Handling vast amounts of data generated by research projects.

Building Off Existing Research Examples

Building off existing research examples is crucial as it accelerates the pace of innovation and avoids redundancy, allowing researchers to focus on advancing knowledge rather than reinventing the wheel. Leveraging established findings provides a solid foundation for new discoveries and fosters a collaborative scientific community.

Quanser offers a comprehensive library of research examples, much like our curricula. These examples come with well-documented code and illustrate various applications you can bring to life using our products.

Quanser Can Speed Up Your Publishing Timeline

The more you publish, the more you get referenced and cited, and consequently, the higher the quality of the journals you get accepted into. This is a crucial KPI for academic career growth, which is directly proportional to your publications and citations.

Using Quanser’s technology, researchers typically expect to publish their findings in as little as three months. Sourcing other equipment might extend this time to anywhere from six months to a year. While the fundamental research and eventual publication could still be of high quality, they often won’t match the efficiency and output possible with Quanser’s solutions.

Make Your Own Robotics Lab or Buy a Turnkey Solution?

Establishing a robotics lab without a clear plan can be a significant drain on time and resources. You might find yourself spending one to two years building it from scratch, which inevitably leads to wasted time, money, and talent. Grad students, your most valuable resource, end up being utilized inefficiently. And after all that effort, the research results might not even be up to par.

What are the Costs of Building a University Robotics Lab?

In terms of costs, you need to have a comprehensive understanding of what the setup will look like. This ranges from the hardware and software requirements to the specific projects and research initiatives you plan to undertake. While it might be tempting to dive into the specifics of the budget right away, ensuring you have a precise and strategic plan will make your investment more worthwhile and effective.

Now let’s consider what goes into it beyond the physical components.

If you’re designing from scratch, you’d need to hire a mechanical engineering grad student and an electrical engineering grad student, potentially spending at least a year on the project. This translates to two salaries already sunk into development.

Despite that investment, the result from what we’ve seen is often a subpar robot. DIY robots are not optimized—sensor readings might lag by up to a second or more, rendering the robot nearly useless.

Quanser’s advantage lies in the high-quality hardware and electronics and in a robust software stack that delivers optimal information to the decision-making elements. We sweat the details to ensure you get the best performance right out of the box.

Conclusion and What’s Next

Establishing a robotics lab involves thorough planning, clear objectives, and collaboration with industry experts. By selecting versatile equipment, securing funding, and developing a robust curriculum, you can create a dynamic and innovative robotics lab that supports both educational and research goals. Stay tuned because we will dig deeper next time regarding how to build robotics labs!

About the Author

Paul Karam is the Chief Operating Officer (COO) and Chief Robotics Officer at Quanser, a global leader in the design and development of solutions for engineering education and research. In his role as COO, Paul Karam is responsible for managing Quanser’s business development initiatives, R&D roadmap, and day-to-day operations. He plays a crucial role in driving innovation in key emerging applications, including autonomy, artificial intelligence, and self-driving cars.

Before becoming COO, Paul served as Quanser’s Director of Engineering for over ten years, where he led the engineering team in developing transformational lab solutions for a global community of educators and researchers. His leadership was instrumental in delivering effective, sustainable, and academically appropriate solutions.

Paul holds an Honours Bachelor’s degree in Electrical Engineering from the University of Waterloo, Canada. His efforts have been particularly notable during the pandemic when he led his team to create virtual lab experiences with digital representations of classic Quanser hardware, ensuring continued meaningful and engaging lab experiences for students worldwide.

The post Quanser’s Roadmap to Starting a Robotics Lab appeared first on Quanser.

The Road from Digital Twin to Grand Stage 

The Quanser Self-Driving Car Student Competition began with an impressive roster of 39 teams from 28 universities worldwide, all eager to showcase their skills in autonomous vehicle technology. The competition’s initial phase saw a flurry of activity as teams from diverse academic backgrounds used QLabs Virtual QCar, the Digital Twin of Quanser’s Self-Driving Car Studio, to submit their conceptual plan based on the given tasks. The virtual QCar and its environment were a fully instrumented, dynamically accurate digital twin of the physical QCar and on-site environment. The top 11 teams that emerged from this stage were those that successfully overcame the controls and autonomy challenges presented to them on the virtual platform.  

As the competition progressed, the field narrowed down to 11 teams that distinguished themselves through their successful algorithm validation on the virtual platform. These teams were poised to bring their theory to life on the grand stage in Toronto. 

From there, the competition moved to the algorithm validation stage, where the teams’ ideas were put to the test on a physical platform, Quanser’s QCar, provided to all the teams. This phase was about proving their algorithms’ viability and refining their designs based on real-world feedback and understanding the nuances of digital Twin versus hardware. 

Self-Driving Car Studio (SDCS) map set up at the main competition area

However, the journey to Toronto was not without its hurdles. Due to unforeseeable travel challenges, only eight of the eleven finalists made it to Toronto ready to compete and demonstrate their practical applications of their months of hard work. The absence of the three teams was felt, but the competition moved forward, celebrating the achievements of the teams that were able to make it and honoring the spirit of innovation that all the participants shared. 

After months of preparation, the competition culminated in Toronto, where the selected teams had the opportunity to compete on-site at the American Control Conference. Here, the teams faced a series of real-world challenges designed to push the limits of their algorithm and their own skills. These challenges were meticulously planned to test not just the speed but the precision of their autonomous vehicles, demanding flawless real-time decision-making, strict adherence to traffic regulations, and the agility to follow the street profiles. The road from Digital Twin to the grand stage in Toronto was a transformative experience for all participants, marking a significant milestone in their development as future engineers. 

The Global Race 

The competition saw a diverse array of teams, each bringing their unique perspective to the table (in alphabetical order): 

• AutoWheels from King Fahd University of Petroleum and Minerals (Saudi Arabia)
• CalPoly Zoomers from California Polytechnic State University (USA)
• CDSL@UoS from University of Seoul (South Korea)
• Fast&Driverless from Czech Technical University in Prague (Czech Republic)
• FullThrottle-SDCN from York University (Canada)
• PolyCtrl from National Polytechnic University of Armenia (Armenia)
• QCARdinals from University of the Incarnate Word (USA)
• VOICE from Kyungpook National University (South Korea)

The winner was promised rewards that matched the event’s innovative spirit. The faculty supervisor of the winning team was to be awarded a QCar and a one-year subscription to QLabs Virtual QCar, collectively valued at $30,000. Moreover, the champions would also receive a $2,000 Amazon gift card to be shared by the students and the prestigious Golden QCar trophy. 

Golden QCar trophy

Transforming Engineering Education Through Competition 

The Quanser Self-Driving Car Student Competition stands as a beacon for the transformation of engineering education. It exemplifies how theoretical knowledge can be applied in practical, real-world scenarios, bridging the gap between classroom learning and hands-on experience. This competition is more than just a test of skill; it’s an educational experience that encourages students to think outside the textbook and apply their learning in innovative ways. 

By participating in this competition, students gain invaluable experience in teamwork, problem-solving, and project management—skills that are essential for any aspiring engineer. The challenges they face in the competition mirror the complexities they will encounter in their professional careers, preparing them for the future of engineering. 

Teams from York University and National Polytechnic University of Armenia competing for the championship

Furthermore, the competition fosters an environment of peer learning, where students can learn from each other’s successes and setbacks. This collaborative atmosphere is crucial for the advancement of engineering education, as it promotes a culture of continuous learning and improvement. 

Quanser’s commitment to providing cutting-edge technology for the competition demonstrates the potential for strategic academic partnerships to enhance educational outcomes. By giving students access to the latest technologies, Quanser is not only contributing to their immediate educational experience but also equipping them with the knowledge and tools they need to drive innovation in the field of autonomous vehicles and beyond. 

Team Fast & Driverless from Czech Technical University in Prague won the championship

Accelerating the Pace of Autonomous Vehicle Research 

The Quanser Self-Driving Car Student Competition has far-reaching implications for the field of autonomous vehicle research. By challenging students to apply their knowledge in a competitive and practical environment, the event serves as a catalyst for innovation in this dynamic field. It provides a unique platform for students to experiment with advanced concepts in sensor fusion, control systems, and real-time decision-making, which are the cornerstones of self-driving technology. 

This competition not only propels the students’ projects forward but also contributes to the broader research community’s understanding of autonomous vehicles. The solutions and breakthroughs achieved here can inform and inspire ongoing research projects, potentially leading to advancements in the efficiency, safety, and reliability of self-driving cars. Moreover, the event encourages collaboration between academia and industry, fostering a synergy that is vital for the rapid development of autonomous vehicle technologies. 

Quanser’s leading-edge self-driving car technology, showcased during the competition, exemplifies the practical application of theoretical research. It demonstrates how controlled environments and real-world scenarios can merge to test and refine the algorithms that will drive the future of transportation. As a result, the competition not only highlights the current state of self-driving car programs at universities but also sets the stage for future discoveries that will shape the trajectory of autonomous driving. 

Teams working hard on their codes and testing their algorithm

A Tribute to Quanser’s Dedication and a Look Ahead to ACC 2025 

As we reflect on the success of the Quanser Self-Driving Car Student Competition, it’s important to acknowledge the immense effort and dedication of the Quanser team behind the scenes. The seamless integration of cutting-edge technology, meticulous planning, and execution laid the foundation for an event that was as educational as it was exciting. The spirit and atmosphere of the event were captured well in this coverage by the local CTV News.  

The Quanser team’s commitment to advancing engineering education and research in Controls, Mechatronics, Robotics, and Autonomy was evident in every detail of the competition. Dr. Jacob Apkarian, Quanser’s founder and CTO, expressed immense pride in fostering the talents of future innovators in this interview. Also, Paul Karam, Quanser’s Chief Robotics Officer and Chief Operating Officer, had a radio interview with 105.9 FM where he shared his thoughts on how the students learned life-changing experiences through the competition and are going to be the future engineers that will make the world a safer place. 

Team Students and Team Quanser

Looking forward, the torch will be passed to the next American Control Conference (ACC 2025) in Denver, Colorado. We invite readers to spread the word and encourage participation in next year’s competition, whether you’re a student with a passion for autonomous vehicles, a university looking to showcase your program’s advancements, or simply an enthusiast of engineering innovation. 

So mark your calendars, start your engines, and prepare to join us at ACC 2025 in Denver for another exciting chapter in the journey of self-driving car research and education. Let’s continue to support and celebrate the bright minds that will steer us into a future where autonomous vehicles are not just a possibility, but a reality. 

The post Navigating the Future: Quanser Self-Driving Car Student Competition appeared first on Quanser.

It was a crisp Sunday Morning in late March and the New Mexico sun was just beginning to heat up the day – I was one of the first talks of the day so I felt obliged to be a little controversial and wake the audience up! There is no better way to get an Engineer’s blood pumping than to challenge their department’s claim to be the ‘best’ . 

My opening slide was the following image:

Growing up as a child of two Engineers (My mother Elise an Electrical grad and my father George a Mechanical grad), I knew very early on about the battle between Mechs and Elecs! I continued that tradition of competition when I decided that I was going to be better than both my parents and become a computer engineer! 

 I was convinced (as sure as any 17-year-old can be) that I was destined to become a computer engineer and write code for the rest of my life…but then as is often the case, life happens:

Fast forward 23 years later and here I am as Chief Robotics Officer at a wonderful company called Quanser: 

For those of you who may not know much about Quanser, we are an academically focused company who for the last 35 years has had a mission of delivering fantastic engineering labs to complement undergraduate teaching and graduate research. Our vision is that engineers need to be given many experiences of bringing theory to life in the Lab as hands-on lab experiences are what differentiates us from other STEM disciplines! 

Although I might have claimed that I was a robotics engineer when I graduated in June of 2000, it has taken me 23 years of applying theory on real systems and iterating on failures and discoveries to authentically call myself a true robotics engineer. In my experience over those years, I can confidently claim and finally appreciate the fact that I didn’t need to be better than both my parents but rather combine their skills and disciplines together to get me to be the best robotics engineer I can be.

While I did graduate from the ECE department, I still fundamentally believe that Robotics is and should always be multi-disciplinary. Our observations over the last 10-20 years were that robotics was a natural evolution of mechatronics and most mechatronics departments were either completely part of mechanical or, at the very least adjacent to mechanical. The classic mechatronics image below shows a balanced Venn diagram of many interdisciplinary engineering specialities: 

As we approach the quarter mark of the 21st century, things have and are definitely changing. The image above of does capture an electro-mechanical specialization but it misses the mark in capturing the direction and growth of current day Robotics.  

To further examine this thought, I did what most students will do today…I asked ChatGPT!: 

I was pleased to find that even ChatGPT is aware of where robotics and robotics engineering should be heading. As our solutions at Quanser evolved from control systems, through mechatronics and now robotics, autonomy and AI, the following diagram has emerged as to how we capture and describe modern robotics: 

We believe capturing and communicating that any modern intelligent system or robot should always iterate around these 4 fundamental elements of: 

• See: Sense the world around you and bring in new modalities of data and information.
• Think: Use your previous memory and fuse it with the new information you have just seen to better update your understanding.
• Do: Use your thinking to decide what to do next, where to go, how to go about it and what energy to utilize.
• Talk: Communicate, Communicate, Communicate. The more robots can communicate with one another and with other systems, the more intelligent they will become.

 When it comes to viewing these functions from a departmental specialization lens, it becomes clear that many of these functions are at the core of all ECE departments. 

There are still many novel and exciting elements for us to discover and engineer in the world of doing (Energy, Propulsion, Sustainability, etc). Having said that, the biggest advancements that Robots will be making in the next decade will come from further developments in the 3 other areas of robotics – which are all squarely at the heart of ECE departments!

The above bullet points simply capture a small sample of where these fundamental elements of a Robotic system will continue to grow and be engineered – and more specifically, they are all core to ECE departments: 

• See: The smart phone has revolutionized our advancements in camera technology as well as inertial measurement units. Tesla and other self-driving car companies are continuing to develop better and novel modalities of sensing the environment around them to better inform their robots of where they are and what’s around them. 
• Think: How many times have you thought about or read about AI this week? We are simply at the infancy of where AI and generalized computing is heading. Finding novel ways to learn and process large sums of data and create novel systems that can make real-time decisions on these large sets of data will be how we transform and automate many of the systems and robots around us.  
• Talk: As sensors and sensor data grow exponentially, so do the need to find ways of capturing, transmitting and storing all of this data. Over and above the localized management of the data, the more robots are able to talk to one another and other systems in their environment, the better and smarter they will generally be. We have demonstrated these increased capabilities and intelligence in many of our Autonomy and AI Labs such as our Self-Driving Car Studio and our Autonomous Vehicles Research Studio to name a few. 

In conclusion (and not completely disappoint my dad), I do still truly believe that robotics is and always will be multi-disciplinary…there is no question in my mind that some of the most important advancements in the field of robotics will come from professors, students and graduates of ECE departments! 

To not completely alienate my other half (Mechanical Engineers), there are and will still be many important robotics concepts that will be driven from ME departments and ME Engineers –

Please stay tuned for an upcoming article on the critical role Mechanical and Mechatronics Engineering departments will play in the future of Robotics!

All of us at Quanser are driven to continue to support and grow with the global academic engineering community and we would love to continue this conversation with you online! Please feel free to connect with myself directly via LinkedIn and also subscribe to our newsletter to stay in touch and receive exciting news on new engineering feats and developments our partner Universities have accomplished around the world! 

The post Dear ECE – The Future of Robotics is in your Hands! appeared first on Quanser.

I am excited to announce that a high-fidelity digital twin of the Qube-Servo 3 with stunning visualizations is now available! It is the latest release in the Quanser Interactive Labs collection of our most popular experiments.

Unlimited access

A classic challenge educators face is related to just how much time is sufficient for practical hands-on lab activities in any course. Managing lab hours for a class of hundreds of students in multiple batches is a non-trivial problem. Even when the logistics are handled correctly, the correct flow in a lab is required to ensure a good educational experience. In my opinion, students must prepare before arriving to the lab with a pre-lab questionnaire or set of exercises or risk missing the big-picture and point of the entire lab. After the lab, students should go through a secondary critical thinking questionnaire to validate the experience they just had, or risk not getting closure from relating that experience to course material and learning outcomes.

For this reason, all our curriculum comes with pre-lab reference material in the form of concept reviews and application guides, in-lab procedures to encourage scientific exploration and point to interesting phenomenon, and finally post-lab recommended assessments to raise critical thinking questions. You can see some sample documentation for the Qube Servo 3 below. Despite this flow, getting access to real hardware often amounts to a couple of hours every other week at best. This is just not enough!

Concept Review	Application Guide
Lab Procedure	Recommended Assessments

Imagine a scenario in which each of your students have access to a digital twin. If you want more information on what that precisely is, here is a good review. Students can first run the lab procedures with this virtual device directly at home at their own pace and time.  They can explore course concepts in implementation as many times as it takes to get to their comfort level before coming to a lab to access the exact piece of equipment in real life. The cherry on top? The code they develop at home to work with the digital twins transfers directly to the hardware. No tinkering required, which enhances their overall lab experience with digital twins.

Exploration

Ever had to fight for budgets to repair equipment or replace it entirely because of a ridiculous control gain that sent a device to its death? It happens, as a natural part of engineering exploration. But it also brings with it an expensive and time-consuming process of dealing with the consequences. Virtual digital twins of real hardware in your lab do not have this problem at all.

Send a virtual load disk flying off the servomotor? No problem, hit the reset button or restart your virtual Qube-Servo 3 workspace all over again. Have a go at it a second time and figure out why it happened. Find critical failure points of your controllers, tune your gains in real-time and let your students get to that understanding before they mess with hardware. Of course, our physical Qube-Servo 3 is student proof as well. However, the first time your students use it won’t be their first time using a Qube. That is something of high value. Check out me trying to tune a swing-up controller to get the pendulum in the upright position using an energy-based method.

Energy-based swing-up controller tuning with the virtual Qube-Servo 3

Closing remarks

Access to hardware is great for students to bridge the gap between theory and practice. Access to virtual digital twins further enhances this experience. I think you should definitely try out a virtual Qube-Servo 3 or in fact, any of our other solutions in Quanser Interactive Labs yourself by requesting a 30-day trial right here. Also look out for scalable and revamped content, for both the hardware and virtual Qube, over the next few months.

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We shared the hall with companies that provide solutions to a variety of unique challenges, from industrial robotic cleaners, robots for applications that pose a risk to humans and rehabilitation robots, to city-wide delivery solutions. We even shared space with the F1TENTH competition, which was a great way to see how autonomous driving vehicles can be pushed to the limit for time trial racing. Take a look at our iROS 2023 team and our booth!

As a company primarily focused on solutions for both teaching and research, we were able to differentiate ourselves by presenting our array of solutions that focus on and cover a wide variety of academic applications. The potential challenges we talked about at the show ranged from improving control systems, robotic manipulator education and warehouse robotics, to self-driving and autonomous drone research.

Our booth at this conference presented our core offerings, highlighted using a variety of different demos. We had a Qube-Servo 3 with its virtual twin demonstrating the building blocks of mechatronics and robotics teaching applications. The QCar and its virtual twin in a city environment showcased the wide variety of applications for our autonomous intelligent systems hardware and digital twins for different scenarios when testing self-driving vehicles. The QArm, paired with the unreleased QBot Platform, demonstrated a collection of robotics algorithms that students could develop, including pick and place, line following, sensor fusion, and multiagent communication. Finally, we had a QDrone 2 and Aero 2 demo to showcase how students can go from learning the basics of flight dynamics and control with the Aero 2 and its virtual twin, to more complicated flight dynamics using our QDrone 2.

Check this video out, where Peter Martin, our Director of R&D, talks about Quanser, our product offerings and what sets us apart!

See you at the next conference!

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