The convergence of AR, VR in Robots is almost like a holy grail that provides an immersive AR or VR environment to operate and control robots for different tasks and actions. Subsequently, this combination can also enhance consumer experiences and customer services across several industries with more personalized, accessible, and immersively designed experiences.
Virtual Reality (VR) and Augmented Reality (AR) are two of the most innovative and groundbreaking technologies of the 21st century. Due to their endless potential and prospects, the world's technology leaders are slowly moving their platform to adapt and incorporate VR and AR technologies.
In addition, the ability of these mixed reality technologies to enhance customer experience and strengthen their engagement has led them to experience rapid growth and adoption among companies and consumers alike.
Alongside this, robots are also making strides across the world in different industries & applications for both various outdoor and indoor scenarios. Robots of different kinds, i.e., industrial Robots, humanoid robots, service robots, specific purpose robots, and many others, are also rapidly empowering industries, businesses, and services worldwide.
Getting to Know the Technologies
Augmented Reality (AR) is a mixed reality experience in a real-world environment where real-world objects and environments are enhanced by various computer-generated perceptual information. The AR system delivers this information through various digital media (visual and sound) elements, haptic feedback, and other sensory stimuli. In addition, the AR system itself powers and provides these multiple digital sensory elements.
On the other hand, Virtual Reality (VR) is a simulated experience in a 3D virtual world environment with computer-generated VR worlds, scenes, and objects that usually immerse the user in their surroundings. The VR system also delivers different sensory information, including sound, visual, haptic, and other sensory input that helps to make the user feel like they are a part of the virtual reality world. A head usually accompanies this system-mounted device known as Virtual Reality Headset, which delivers all the user's visual and other sensory information.
A robot is a machine that is generally programmed to do specific tasks and actions automatically. Industries and businesses often use robots in an environment requiring them to complete a series of complex charges and actions repeatedly.
Robots today are usually automated for industry environments like a supply line, production chains, and manufacturing processes. Still, they are slowly albeit steadily making strides across different industries worldwide, even for general purpose tasks and actions.
How does this Convergence Improve Customer Service?
AR & VR are immersive technologies, while robots are machines that enable automation of complex tasks & operational control over themselves to allow humans to guide & use them. The convergence of these technologies has a huge potential to break barriers to what we can achieve in terms of immersive technology, R&D, enhanced experiences, and different manufacturing & production problems.
VR and AR companies like FS Studio already provide advanced solutions regarding R&D technology and product innovation to enable rapid development through the convergence of these technologies.
VR and AR technologies are proving to enable much better consumer interaction. Various companies can use AR or VR visualization of their products for marketing even before it is ready. In such cases, the products are usually more accurate and close to their real-life counterparts than the pictures of the products.
Images can be misleading or inaccurate from their real-life counterparts due to scale, angle, color corrections, and lighting. However, consumers want accurate real-life-like visualization of the product. So, VR and AR are beneficial for this purpose.
With this, VR and AR can enable product customizations since VR and AR representations can be easily changed and modified without affecting its realism or accuracy from the actual product. It allows a whole new level of product selection experiences for the consumers. It leads to enhancement in consumer experiences, interaction, and engagement, eventually leading to more satisfaction and conversion rate.
Mix these advantages with robots; you can enable a whole new category of customer service and engagement across different industry platforms. Currently, other industries use robotics for faster production lines equipped with automation.
But with increasing adoption among consumers and advancement in robotics, various commercial robots are also rapidly growing. Different robots like humanoid robots, unique purpose robots, dancing robots, robotics for children, and robots for entertainment are currently seeing high demand along with the consumers.
It would eventually lower costs related to product manufacture, improving products much faster and in a more refined way. It would also lead to lower prices of the products among consumers.
Apart from this, customer support in numerous markets with more personalization and immersive experience is possible. AR and VR can converge customer service with robots to enable various advantages. Workplaces under dangerous conditions will be benefiting mainly from this kind of technology.
Here, workers would be able to use VR-controlled robots or VR-controlled drones in dangerous environments as if the workers were in those environments due to immersion provided by AR or VR. In addition, it would make the work more efficient and precise in comparison with traditional ways of robot control.
VR and AR tech is actively in use, and robotic technology is across industries like healthcare, manufacture, the military, and industries dealing with dangerous work environments. VR is generally used for better control and operation of robots by a human operator for various tasks.
On the other hand, AR is usually in use for enhancing real-world environments. AR robotics is also in use for healthcare facilities to provide disabled patients with robotic exoskeletons.
Adoption of this Technology across Different Industries
Virtual reality businesses are breaking the barriers with virtual realism, while augmented reality businesses redefine mixed reality. Industries that require robotic education or training can reap huge benefits from this type of technology.
For instance, the military can use (Russian Military uses VR trainers to train their soldiers) VR and AR technologies to train soldiers, pilots, and vehicle drivers without ever stepping into the battlefield or aircraft or vehicles. Moreover, it is possible due to the amalgamation of VR with robotics to achieve real-like virtual simulations for soldiers to train in.
Augmented reality in the military can also work with robotics for different training and strategic purposes, i.e., robots enable sensing environments and data collection. At the same time, AR enhances the setting for the soldier to train into.
VR & AR can also blend well with robotics for investigative purposes or in law enforcement services. For example, using AR and VR robots to investigate any crime scene repeatedly without being present on the stage will substantially positively impact the accuracy of the verdicts on various cases and criminal charges.
Robots with VR and AR can help with R&D and product development. Enabling rapid development, design, testing, and iteration of products through simulations and 3D models, VR and AR can assist robots in manufacturing plants or product lines for a more efficient, accurate, and cost-effective supply chain. It also increases safety and security around manufacturing industries since robots with VR and AR capabilities can take on more dangerous and harmful tasks.
Robots like drones or robots with locomotive capabilities are useful with AR & VR to scan and generate 3D models of various environments and objects. FS Studio's own ZeroSim technology enables this kind of innovative tech. ZeroSim is a robotics simulation engine that can be used in the rapid development of robotics and simulations in various environments and industries.
This convergent tech is also helpful for robots with object recognition since it requires 3D models for more appropriate and enhanced detection and scanning of objects.
Furthermore, data collection with real-time communication among robots with the help of AR & VR will also be very efficient, fast, and cost-effective. It shows that VR and robotics can merge very efficiently.
The Healthcare industry can also use this type of technology. For example, health care facilities are already using robots with VR capabilities to perform remote surgeries and the training of surgeons. Equipping these robots with AI would enable unseen precision and accuracy in the surgical process. In addition, AR is beneficial for scanning the body and visualizing various digital elements to assist and enhance the surgical procedure, while VR enables remote surgery.
Patients with disabilities can also use AR-based exoskeletons like augmented arms or prosthetic limbs to assist themselves. It shows that augmented reality and robotics align very well, even in the healthcare industry.
The high development of VR in different industries shows that the adoption of virtual reality in business worldwide is in high demand. Additionally, augmented reality in business is also in need due to its advantages and benefits.
The combination of Augmented Reality, Virtual reality, and Robots opens a technological path that has a vast possibility and capability to enhance and improve customer services in various industries.
Furthermore, VR and AR robots are arising with numerous advantages and benefits for both enterprises and consumers alike. With opportunities to grow even more influential and eventually mainstream, the future of AR and VR technologies and its convergence with robotics looks very bright.
Various businesses, organizations, and companies across multiple backgrounds from general-purpose industries, manufacturing industries, military to enterprises working in dangerous environments, and even healthcare industries are widely adopting this technology to enhance their services and provide more engaging, interactive, and improved customer service.
AR and VR Technologies Guide Robots to improve their manufacturing process. From the assembly line to fieldwork, there is always a need for better technology that will help with efficiency and accuracy.
Augmented Reality (AR) and Virtual Reality (VR) technologies can provide some of these solutions. Industries like automotive, aerospace, medical device development, military training simulation, etc., have already used AR/VR technology.
Still, it can also be applied to robotics end-to-end design processes, including concept generation, prototyping, and testing on-site or remotely in immersive environments. Perhaps one of the best uses for AR and VR in robotics is to make training easier.
Here are some current and potential applications of AR and VR in robotics:
Augmented Reality to Improve Human-Robot Communication:
Robots have long been used to automate tasks in the workplace, but now they're evolving beyond just being mindless laborers.
As such, humans must interact with robots while performing various work-related activities and functions that were once left up exclusively between machines themselves.
This new trend has come about due essentially because these high tech devices are becoming more advanced than ever before - capable of doing things like learning on their own
The future of work is coming sooner than you think. With robots replacing humans in many tasks, it will be necessary for the two groups (robotics specialists and humanity) to find common ground to communicate with each other effectively.
At the University Of Boulder Colorado's robotics lab, researchers have found an innovative way by implementing augmented reality technology into drones to allow users to figure out how robots and humans could communicate in different targeted working spaces.
The researchers concluded that they could improve human-robot interaction in co-working spaces significantly by using AR & VR.
Virtual Reality Trains Robots for Object Identification:
A robot that can learn and predict behavior on its own through data exposure is an exciting idea in artificial intelligence.
A robot's learning process is not limited to humans. It can also take in data and learn how to group it with similar categories, discriminate between different items or recognize new ones that look like those the bot has been exposed to before.
Researchers at the University of California, Berkeley succeeded in training a robot to pick up objects indicated by them after being introduced with different items through virtual reality.
One example shows how robots are becoming more advanced and can assist people who need them around their office space or factories.
Using VR, robots can be taught anything with the least amount of cost and effort. In addition, it means that trainers only need a 3D model for their virtual Reality training sessions - not real-life models!
That's an incredible convenience because it allows them to explore large-scale territories without physical constraints on time or space like in traditional fieldwork situations.
Medical Robots Equipped with AR:
Robotic surgery is already being performed in hospitals worldwide, but there's more to it than meets the eye. For one thing, advanced robotic arms can be found performing delicate surgeries or assisting with other medical tasks like drug manufacturing facilities.
These machines can provide direct assistance while ensuring safety during an operation by monitoring vital signs remotely and autonomously navigating through obstacles independently.
The students at Monterrey Institute of Technology and Higher Education in Mexico created a robotic exoskeleton that can assist people with mobility problems to stand and move around the house or office freely without fear that they will fall on their own.
This outer body was equipped with augmented reality capacities so its human operator could view each part while deciding where it would best fit for optimal use!
Programming by Demonstration Taken to the Next Level:
Robots need to be programmed with complex tasks such as dangerous ones before they can perform these actions. So, robots are programmed by demonstration.
Programming by demonstration is similar to employee training: the human operator demonstrates a task until it can replicate it on its own or teach an existing robot how to do something new.
However, this isn't always possible since there are some things even machines cannot learn without being shown first-hand through direct instruction from someone who knows what needs doing best!
AR and VR technologies allow designers to create the entire demonstration in a virtual environment or superimposed over real life.
For example, OpenAI robots developed by Elon Musk's company trained robots using Vision Network. This technique is effective with swarm robotics too!
Robotics and AR technology in Manufacturing:
The use of robotics and AR technology will have a significant impact on Manufacturing. It allows manufacturers to show their design, building site, or product faster without much hassle.
VR comes as a standard preparation method for people who face physical hazards, such as 3D printing models or simulations that can reduce the need for costly modeling equipment and improve product quality in the workplace.
AR & VR models in Urban planning:
With new construction projects becoming more popular, developers need to understand how these elements will impact current urban environments.
So, they can maximize profit over all-time horizons by generating AR (Augmented Reality) and VR-modeled before building anything at all.
Integrating virtual reality and robotics for construction employees can have immense benefits. The idea behind this is to allow humans more creative space while robots take care of repetitive tasks.
In this way, humans can focus more on the implementation of strategies for better outcomes.
Use of virtual reality in military training programs:
The use of virtual reality in military training programs can help soldiers understand how to use military robots and drones.
For this purpose, organizations create a virtual battlefield that allows military officers to test different tactics on an interactive landscape with realistic visuals for each drone's move.
For example, the military trainer can turn his head left or right or move it forward or change the altitude levels on one's drone.
With this approach, soldiers can learn to avoid various obstacles and guide a drone through the battlefield.
Virtual reality also helps them to teach how they might tackle situations on an actual field of battle or attack enemy bases at just about any given moment with their robots in tow!
Robotics & VR work hand-in-hand so that we may develop more advanced training programs for our warriors--both human beings and bots alike.
AR and VR in Crime Department:
AR and VR are changing the way that we think about crime scenes.
Forensic experts can use these technologies to label evidence at a time digitally. For example, AR annotation allows forensic investigators to digitally label proof traces at crime scenes and update, exchange, and transfer lists of evidence.
On the other hand, VR technology maps training goals explicitly and develops & tests a virtual prototype to train agents to adapt to challenging work circumstances.
VR in Teleoperating Robots:
Virtual reality is being used to design teleoperating robots that can eliminate dangerous factory work environments.
This type of robot involves replicating what we do with our hands; they can be controlled remotely and act accordingly with the help of hand controllers and multiple display sensors.
For instance, FS Studio has built VR Teleoperation for Robotics for a major car company. We developed an immersive virtual reality environment for the client's training and remote operation of the robotic system. We even integrated two types of haptic controllers in the process!
We successfully created a Unity 3D environment that would import robots using URDF files. The resulting VR environment had terrific functions.
For example, it could control the robot, display the data and video from the various sensors and cameras, determine path and route navigation, and show all of this in an immersive 3D VR environment.
The future of robotics is here, and it has never been brighter. Thanks to the advancements in new technologies like AR and VR, we will soon see robots that can think for themselves and work with humans as partners.
We've given you a quick overview of these cutting-edge innovations but know there is so much more out there just waiting to be explored!
Combining simulation and AI technologies like Machine Learning & Deep Learning unveils outstanding new possibilities and opportunities. Moreover, the use of AI on traditional approaches to simulation may even bring forth a paradigm shift in the industry regarding how we perceive and develop the simulation.
Although simulation and Artificial Intelligence (AI) are two different technology paradigms, these technologies are related to each other in their primary forms. In computer engineering, simulation imitates an environment or a machine, while AI effectively simulates human intelligence.
While they may be related, simulation and AI were being used very differently with different mathematical and engineering approaches. However, in recent years, the development of AI-based simulations has experienced rapid growth in various industries.
For instance, now infamous, Cyberpunk 2077 used AI to simulate facial expressions and lip-syncing in the gaming industry. On the other hand, Microsoft Flight Simulator 2020 used AI to generate realistic terrains and air traffic.
The power of AI to enable rapid simulation development with faster, more optimized, and less resource-hungry simulations even on a large scale would empower more applications of simulation technology in far wider industries and platforms.
However, to understand the benefits of using AI in simulations and its development, we need to understand the traditional simulation development approach and its use in this scenario at first.
Traditional Simulation vs. AI-based Simulations
The basic idea behind simulation development is to gather data related to the machine, environment or anything for different inputs and conditions. These data would then be collected, analyzed, and studied to understand how the machine/environment/anything simulated functions and behaves under different conditions and situations.
This understanding would then be used to build a basic mathematical model that can govern and imitate the actual object in different conditions, then used to construct a simulation model that can replicate or simulate the real thing.
However, when AI is used to build these simulation models, the AI has to be fed with data related to the object/environment's behavior and how these subjects (object/environment to be simulated) function under different conditions and settings. During this process, the AI model requires relevant data that can be considered a sample of the simulation subject and represents the subject properly.
Generally, Neural Networks (NNs) would be used as the AI model to be trained. After the training, this would simulate the subject and its behavior.
Both approaches, either traditional approaches or AI for simulation, have their advantages and disadvantages. One of the significant advantages of the conventional simulation method is that the mathematical model defined after studying the simulation subject can be reused and reconstructed easily.
This allows other development teams to verify or reuse the same mathematical principles or models to generate the simulation. A traditional approach would also enable the developers to expand the simulation based on their understanding of the subject without explicit testing or proof test.
One of the significant disadvantages of this traditional approach remains to be its complex and resource-hungry process to generate the simulation. This is because everything has to be done by the simulation developers, who would also have to be experts in respective domains such that they need to understand the subject very closely.
Meanwhile, in AI-based simulation development, data is one of the essential components. The subject's information needs to be in abundant amounts and deterministic such that the data can represent the subject very closely.
This type of data may not be available readily when the data needs to be either collected or generated. But after the collection of accurate and abundant data, an AI-based/aided approach is very advantageous since there is no need to understand the subject by developers themselves.
Another significant advantage of the AI-based simulation holds within the power of AI to discover patterns or behavior in subjects not even considered or found by the developers. Apart from this, training an AI model usually takes a lot of time, but it may not be as resource hungry, complex, and costly as the traditional approach.
One of the significant disadvantages of the AI approach is that the model builder cannot be recognized or understood by developers in any way, so it cannot be usually reconstructed unless similar data or input is fed again to train the model.
Apart from this, due to the data required to qualify the model, expanding the model will generally be impossible without sufficient data.
Combining Simulations and AI
Using AI in simulation generation or development would enable data-powered development with rapid changeability and minimal human involvement. Although the simulation traits would be considered too complex for humans to develop, AI may easily reconstruct such characteristics if sufficient data is provided.
Due to this, AI can be used to simulate something too hard, complex, or time-consuming for humans in a short time without too much effort. Thus, not only would the development of simulations be faster, more productive, and easy, but AI would also enable the rapid iteration and tweaking of simulations that would be far less feasible, especially on a large scale.
We can open new doors by combining the power of AI and simulation for product design and development. Generally, without AI simulations, developers have to design a product/model that must be intensively tested before production, and changes are needed after the story. Then, the same process would have to be repeated.
This process is very resource-intensive. But through AI, design changes and validation can be easily tested through simulation, enabling rapid iteration and development.
The development and adoption of AI for simulation are far more required in industries like Augmented Reality (AR) and VR (Virtual Reality), where the sheer complexity of building high scale models, environments, and graphics through the traditional methods would be infeasible compared to using AI to develop and deploy simulations with its data-driven approach of development. The opportunities in AR and VR could be far more explored and matured through the AI to generate and develop simulations.
Alongside this, simulation of subjects like fluids (air and water) is brutal to construct with only a traditional approach, the result of which would still not be good and very close to reality. But with the help of AI, such simulations would be closer to reality and more refined.
One of the significant advantages of AI-based simulation compared to the traditional approach is that the conventional system would be significantly resourced heavy since it usually calculates each simulation particle.
However, AI-based simulation would enable such complex simulations easily since AI can perform these calculations/predictions much faster and less resource hungry. Alongside this, generative simulations like the generation of models, terrains in games, and product designs would also be possible with AI.
For instance, take the game Microsoft Flight Simulator 2020 as an example. This game allows gamers to experience realistic flights worldwide without lagging in the quality of models, terrains, and environment.
By traditional approach, this would mean that the game developers would have to model and build all terrains used in 3D along with matching landscapes and backgrounds to give the simulation a realistic feeling.
This would have cost the game developers a massive amount of time, resources, and a considerable number of experts to deal with complex problems lying ahead in such an enormous project. Realistically, such a project would not be feasible or even practically be possible to complete.
But through the use of AI, the developers used massive amounts of data that are already available and combined them with vast amounts of computation through the power of the cloud to train an AI model that could build realistic 3D models of terrains, environments, along with grasses, trees, and water-based upon the real world.
The results produced were pretty spectacular and received substantial critical acclaim from game developers and gamers alike.
By combining simulations and AI, we can unfold new opportunities and endless possibilities in different industries.
Along with technologies like Machine Learning and Deep Learning, AI-enabled simulations will be propelled by the data-driven backend. Conquering the disadvantages of the traditional approach to simulation, AI-based simulations will be able to push the boundaries of what simulations can do.
Even the most complex simulations, which would be next to impossible when developed with traditional methods, will be attainable by combining simulations and AI.
Moreover, with AI enabling rapid development of more optimized and improved quality, the industry may experience a revolution empowering next-level simulations with realism and details never seen before.