Join our upcoming webinar “Deriving Business Value from LLMs and RAGs.”
Register now

Computer vision applications in robotics

Thank you for subscribing to our newsletter!
Oops! Something went wrong while submitting the form.

The way forward promises a greater diversification of activities performed by robots. Robot vision is much more flexible and capable of replicating or replacing humans in many ways. Despite the fact that most robots are designed to perform particular tasks, the end goal is to assemble universal robots that provide a broader palette of functionalities. In this context, the use of the machine and computer vision algorithms to visualize, contextualize and act upon the changes in the environment can be paramount. Are computer vision and robots a perfect pairing, though? We'll find that out as we proceed with this blog post.

cv in robotics

What is robotics?

Robotics is a branch of technology that integrates computer science and engineering while dealing with physical robots. The latter is equipped with sensors to visualize and perceive the surroundings and effectors to interact with the outside world. Computer vision relies on these sensors a great deal to give robots the ability to “see” as well as target objects of interest. But how does computer vision differ from robot vision, if at all?

Robot vision vs. computer vision

These two are often erroneously perceived as one. Robot vision is one of the latest innovations robotics and automation technology pride themselves on. In its broader definition, it enables robots and other machines to see. Robot vision is made up of algorithms, cameras, and any other hardware that helps robots develop visual insights. This allows machines to carry out complex visual tasks, such as a robot arm programmed to pick up an object placed on the board. In this case, it will use sensors, cameras, and vision algorithms to perform the task.

Computer vision, on the flip side, aims to give computers the ability to see by building algorithms that process digital images or videos. It is mostly concerned with image classification, object detection, tracking, and pose estimation. However, computer vision and its implementation in the robotics industry is multi-faceted, which we will explore further in the coming sections.

Why computer vision in robotics?

If you still have a question as to why robotic vision is not enough, keep in mind the following: robotic vision can contain elements of computer vision. And we know that visual data processing is a must for robots to perform instructions. The ultimate computer vision integration in robotics covers a broader spectrum of disciplines and reappears across categories. From medical science and autonomous navigation, up until nanotechnologies turn to robots to capitalize on daily operations. This points to the abundance of layers existing within the conditional “computer vision applications in robotics” heading.

Common applications

Visual feedback is essential for image and vision-guided robots. Their power of sight is one of the elements that make them widely used across different disciplines. By and large, the applications of computer vision in robotics include but are far not limited to the following:

  • Space robotics
  • Industrial robotics
  • Military robotics
  • Medical robotics
  • Warehousing and distribution

Space robotics

Space robotics is quite a general category. It mainly covers flying robots that can be multi-purpose and incorporate elements of:

  • On-orbit servicing
  • Space construction
  • Space debris clean-up
  • Planetary exploration and mining
space robotics

The main challenge for these robots is the continuously changing environment, which hinders faster inspection, sample collection on a larger scale, colonization of celestial bodies, and other activities. Although space initiatives are often quite ambitious, the use of computer vision promises streamlined solutions even for space exploration purposes.

Military robotics

With computer vision integration, the door is open for robots to take on a broader range of tasks to assist in military operations. The recent estimations confirm that global spending on military robotics will be $16.5 billion in 2025, and for a good reason: the augmentation of computer vision into the military robots provides undeniable added value. Robotics went from luxury to a necessity, coming to the point where vision-embedded robot operations allow for the following:

  • Military robot path planning
  • Rescue robots
  • Tank-based military robots
  • Mine detection and destruction
military robotics

The latest generation of robotics promises more advanced functionalities and a wider spectrum of capabilities inspired by the human workforce.

Industrial robotics

Within a few years, any task requiring human intervention can be partially or entirely automated. So, it doesn't come as a surprise that the evolution of industrial robots pins hopes on computer vision. These days the list of industrial tasks executed by robots is no longer limited to a robot arm. Odds are George Charles Devol (often referred to as the father of robotics) would be proud of this list:

  • Processing
  • Cutting and shaping
  • Inspection and sorting
  • Palletization and primary packaging
  • Secondary packaging
  • Collaborative robotics
  • Warehouse order picking
industrial robotics

Added to that, the increase of interest from industrial sectors in computer vision robotics has many benefits: first, robots reduce the cost of production long term. Second, they offer better quality and increased productivity through R&A. Third, they enable higher flexibility in production and respond to the shortage of employees in the fastest way possible. The drivers above trigger trust and further investment in robotics and computer vision-inspired automation solutions in the industrial sector.

Medical robotics

The analysis of 3D medical images by computer vision fosters diagnosis and therapy, but computer vision applications in medicine do not end there. Robots are especially helpful in surgery rooms fulfilling operations in three categories: pre-op analytics, intra-op guidance, and intra-op verification. To be more specific, they can use vision algorithms to do the following:

  • Sort surgery tools
  • Stitch tissues
  • Plan surgeries
  • Assist diagnosis

Short and on point, they make sure that the surgery design and respective steps match the actual implementation for the brain, orthopedic, cardiac, and other surgeries.

Collaborative robotics

With collaborative robotics, humans can work together in the same place as robots, making the entire process much easier and less time-consuming. However, it is important to note that usually, these processes that involve both humans and collaborative robots are small and are done for specific tasks. A well-known example is Amazon's warehouse robots, which coexist with their staff in their centers. The sole job of these robots is to bring items to the staff, so they pack and label them for dispatch.

Warehousing and distribution

As more people shop online, the not-too-distant past of warehouses and distribution centers turned to robotics solutions to automate picking, scanning the barcode, tossing the products, and other stages of distribution. Standing all day to sort and pick goods is neither convenient nor efficient in this day and age. To bring an example from one of the leading players, Amazon purchased Kiva systems, yet in 2012, was able to move shelves of products without human intervention. Soon FedEx and Ocado also turned to their own AMRs years later, and these were only the first waves of automation. Drawing parallels with today, moving objects from one shelf to another is one of the most primitive robotics challenges, and there's still a way to go to completely automate the warehouse and distribution industry.

robotics in warehousing and distribution

Computer vision challenges in robotics

The next generation of robots is expected to overrun their traditional counterparts in terms of the provided skillset. The computer vision robotics combination is already a huge step forward and is bound to transform the technology. However, the abrupt advance in automation and the increasing demand for collaboration with humans pose several challenges for computer vision robotics.

computer vision in robotics

Identifying moving objects: It's integral to consider three situations when it comes to robot movement:

  • The robot is moving, and the object is static.
  • The object is static while the robot is in motion.
  • Both the robot and the object are in motion.

Making sure the robot operates in content with all three situations is time-consuming and costly, yet makes up for the effort, providing higher levels of performance accuracy.

Identifying covered or partially visible objects: Occlusion (when the target is not visible on the frame and cannot be detected) is quite common in robotics and computer vision. To combat occlusion, it is significant to build an algorithm that connects the visible part of the desired object with the stored image.

Recognizing deformation and modified shapes: Robots will have problems detecting deformed objects without an advanced visual mechanism. Being able to detect crashed vehicles, for instance, can be pivotal, especially for the automotive industry and defense.

Recognizing the position or orientation of objects: Robots require a comprehensive sense of orientation to perform one of the primary tasks in manufacturing or industrial robotics: pick-and-place. Identifying 3D orientations can be especially challenging due to the lighting conditions, the difference in colors, texture, motion, etc. All these have to be taken into account to program a performing robot with a robust vision system.

What is machine vision?

Machine vision is the manufacturing use of vision for automatic check-ups, process control, and governance of robots. Oftentimes confused with machine learning or computer vision, machine vision is quite different, as it is often referred to as very particular applications as opposed to just techniques. Machine vision has a significant role during every stage of the manufacturing process.

While most of the other applications are often scientific domains, machine vision is rather an engineering domain, and even though machine vision uses the same techniques and algorithms as computer vision and image processing yet it uses them to guide robots,  which is exactly how robot vision utilizes it today.  Machine vision combines both software and hardware for operational guidance, marking itself as an integral part of systems such as product assembly.

What you get with machine vision

Robotics: Robots that are equipped with machine vision can have a better understanding of their surroundings and offer greater accuracy.

Manufacturing: Machine vision has a vital role during each part of the manufacturing process, mainly in making operators more effective, data detection, inspecting packages, scanning barcodes, and ensuring the worker’s safety.

Industrial vision: These systems act as a link between cameras and computerized treatments, ensuring product integrity, enabling assembly guidance, detecting peculiarities, and much more.

Benefits of combining robotics and machine vision

With machine vision’s visible advantage over robotics, they are being used in many different applications. Let’s explore some of the most common uses.

benefits of combining robotics and machine vision


When combined with machine vision, robotic systems can now have precise pick-and-place capacities, figure out the important assembly pieces from the storage, and set them in their correct locations.


Robotics can utilize machine vision to detect things, enabling them to identify and classify a larger number of items. With such properties, robots get to do production way faster and improve retail processes.


When it comes to inspection tasks, machine vision can complete them in a faster and more accurate manner than humans, making the whole process more profitable. That being said, robotics can now easily spot important visual components and any sort of labeling mistakes through quality control.

Locating and transporting parts

Machine vision is being used to interpret environmental data so that the robot can comprehend its next moves. With machine vision, a robot can process visual data better, letting it determine the important parts.


To operate effectively, robots must master how to move safely in different environments, and that is where machine vision is deployed. When a robot is wary of its surroundings, it operates more effectively. If robots obtain such skills, it can benefit various industries, such as mining, automotive, and manufacturing.

Final thoughts

Robotics does not cease to revolutionize the world around us. It has penetrated almost every field one may think of. With similar control over human operations and activity in the world, it becomes almost essential to have some kind of automation or human substitutes to assist in daily tasks. These are impossible without visual feedback and ultimate computer vision integration into robot-guided interventions. We hope this article provided you with a complete overview of computer vision applications in the robotics industry. Feel free to reach out if we can be of further assistance.

Recommended for you

Stay connected

Subscribe to receive new blog posts and latest discoveries in the industry from SuperAnnotate
Thank you for subscribing to our newsletter!
Oops! Something went wrong while submitting the form.