Industry 4.0 paved the way for technologies like AI and real-time analytics in the manufacturing domain. Now, the gradual advent of Industry 5.0 will spell the next phase of the industrial revolution. 

In effect, Industry 5.0 is all about augmenting the capabilities of both humans and machines by enabling seamless collaboration. It answers the question of “what technology can do for human workers.” At the core of this discourse, manufacturers plan to improve productivity through human-machine collaboration while reducing the time taken to complete any task. 

In that light, Amr Adel raises a pertinent point about Industry 5.0 in his article Future of Industry 5.0 in Society published in the Journal of Cloud Computing. Adel outlines that Industry 5.0 will “decrease emphasis on the technology and assume that the potential for progress is based on collaboration among the humans and machines.”

It’s here that questions pop up — how exactly would manufacturers go about measuring productivity in the new age of human-machine collaboration? And how would they identify gaps and drive improvements? Before we answer this, let’s understand human-robot interaction (HRI) in its entirety.

Clearing the Air Around Human-Robot Interaction 

Simply put, human-robot interaction (HRI) is the field of understanding the interaction (or collaboration) between humans and robots. In today’s age of Industry 5.0, HRI plays a crucial role in delivering personalized services. 

In the purview of modern manufacturing, robots are more advanced. We’ve come a long way from the first robots installed by General Motors in 1961. Among the best examples, Symbio Robotics has developed robots for vehicle manufacturers like Ford and Toyota. Besides performing spray painting and welding, these robots can install vehicle components, test automobile systems, and check for defects. 

All in all, human-robot interaction is critical to improving productivity across industries like manufacturing, healthcare, agriculture, and space exploration.

Achieving Productivity in Human-Robot Interactions (& Measuring It)

Industrial robots are a critical part of companies looking to improve their production efficiency. There is a growing industry demand for “intelligent” robots that can collaborate with humans to perform a wide range of functions. That said, here are some ways in which human-robot interactions can help in improving productivity:

Human-Robot Synergy

By synergizing humans and robots, companies can improve efficiency and precision. Typically, robots excel at performing repetitive tasks and reducing errors. Alongside robots, humans can utilize their problem-solving and creative skills to optimize business processes.

Workplace Safety

Industrial robots are designed to work in hazardous and physically demanding conditions. By delegating high-risk work to robots, companies can minimize the chance of injuries to workers, thus elevating workplace safety.

Customized Manufacturing

With the growing demand for customized and personalized products, manufacturers are endeavoring to create reliable and intelligent work environments. Through human-robot collaboration, manufacturers can “customize” their facilities where both humans and robots work together for faster product cycles and better productivity.

Streamlined Processes

With human-robot interaction, companies can optimize business processes and resource allocation. Automated processes can free up human workers to focus on more productive tasks. At the same time, robots can reduce production costs and improve operational efficiency.

It’s noteworthy that all these facets (for example, synergistic potential of humans and robots, customization opportunities across the board, process streamlining, etc), define productivity and lay out a platform for measuring it. However, when striving to measure it, it’s best to go forward with a multifaceted approach that takes both qualitative and qualitative factors into account.

Going for Quantitative Measurements

The key here is to leverage real-time data analytics and analyze the operational improvements against metrics, such as output per hour, percentage of downtime, rate of defects pertaining to specific equipment, etc.

Going for Qualitative Measurements

Of course, qualitative insights are also critical to better complement the quantitative results. It’s here that soliciting feedback from employees about specific machine-interaction facets can prove invaluable. For example, the feedback could be targeted at comprehending the:

• General perception and comfort of working alongside machines
• Productivity gains in line with how machines have amplified individual and group productivity levels
• Perception about safety and trust in how machines carry out task under human supervision and automatically
• Particular tasks that are better suited to human-machine collaboration
• Future possibilities of making the human-machine combination more pervasive

Now that we know how to identify gaps and put productivity measurements to use, let’s understand how to improve human-machine collaboration and eventually improve productivity.

How To Improve Human-Robot Collaboration

Modern-day robotics is all about improving the way humans work with robots. With technologies like computer modeling and artificial intelligence, robots can understand and adapt to human users. This means adapting to new environments, tasks, and scenarios. For example, a physically challenged homeowner can use a robotic drone to check his garden or household appliances.

For real-world challenges, human-robot collaboration centers around three focus areas, namely:

• Cognitive or how humans process external information
• Human perception and emotion or how humans learn to trust and share information
• Physical or building the right technology for HRI

How Computer Vision Technology Can Improve Human-Robot Collaboration

Among the latest AI-powered technologies, computer vision is transforming human-robot interaction. Computer vision provides robots with the ability to interpret visual information and perform complex tasks.

Here are some of the real-world use cases of computer vision in modern robotics:

1. Navigate Complex Environments

With the aid of computer vision, mobile robots can easily navigate through complex environments. Using connected cameras and sensors, these robots can identify obstacles in their navigation path and map their surrounding environment.

2. Perform High-Precision Tasks

When enabled with computer vision, robotic arms can perform intricate tasks with precision and accuracy. For instance, they can easily locate objects, grasp them properly, and manipulate the object based on programmed instructions.

3. Develop Human-Like Perception

Computer vision technology can endow humanoid robots with human-like senses and perceptions. For instance, they can track human faces, understand their facial expressions, and interact with them socially. Robots can efficiently respond to human cues, thus enabling better human-robot collaboration.

4. Develop Product Assembling Capability

With accurate object detection capability, computer vision in robotics can help robotic systems in precise “picking and placing” of products. For instance, they can detect the most critical assembly pieces from the inventory and move them to the correct location.

The Bottom Line

Industry 5.0 has ushered in the new age of human-machine collaboration. Among the key challenges in HRI, organizations are looking to streamline communication between humans and robots. This is where computer vision technology plays a crucial role. With CV-enabled systems, robots can “visualize” their surrounding objects and environment.

At KamerAI, we put computer vision technology to work on a variety of real-world use cases across industries. Our computer vision solutions can transform your manufacturing facility into a smart factory optimized for Industry 5.0.

Interested in learning more about how computer vision technology improves productivity in human-machine interactions? Get in touch with us today.