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The relationship between humans and robots will soon deepen, thanks to affective computing, the overarching term for the study, technologies and development of systems that identify, process, and simulate human feelings and emotions. Today we have rudimentary capabilities in software and hardware to perform sensory responses within given environments. As robots are taught how to respond to human emotions, and as engineers abstract lessons from human evolution about how people see, move, balance, hear, and feel, we will be able to better understand how cognition works. The sensing abilities of robots will evolve, and automation ecosystems will expand.
These advancements mean that automation ecosystems currently operating in controlled environments, such as a factory floor or a space satellite, will be adaptable to more chaotic and unpredictable environments — think autonomous vehicles driving on busy city streets, or humanoid robots helping the disabled navigate crowded sidewalks. This expansion of the automation continuum, which involves many different players, rapidly evolving technologies and reams of data, comes with many challenges.
The McKinsey Simplification Model
To facilitate automation adoption, value, and further growth, McKinsey, the global management consulting firm, has developed a model that synthesizes industry recommendations into a single concept — simplification in three essential areas.
Simpler to Apply
Robot developers and integrators need to make it easier for potential end users to envision compelling scenarios. Simplification in this realm could mean something as basic as providing software that closes the gap between conceivability and installation, helping end users prove their design concepts before committing to a final investment.
A prime example comes from ABB Robotics, where visitors to the company’s website are given access to a build-your-own cobot application. Working with intuitive menus, users can browse for functions they need, with options including part handling, screwdriving, visual inspection, and “tell us more.” Users go on to select how the cobot picks up parts and puts them down; where its vision sensors are placed; what communications protocols are used; and whether the cobot will be mounted on a wall, table, or ceiling. Illustrations clarify the choices throughout. Once completed, the program evaluates the selections, then delivers a customized video simulation of how the cobot, fully installed, would perform.
In short, the robots have access to more data for analysis and decision-making. Then edge computing opens the door for even more intimate collaborations between machines and between man and machine.
Simpler to Connect
McKinsey advises that robot manufacturers need to deliver secure, flexible connectivity. A key goal is to achieve interoperability. The robots should be able to readily connect not only with other robots but also with the full range of intelligent systems, edge, cloud, analytics, and similar tools and devices.
Cobots rely on multiple sensors and tools such as AI to make sense of and operate safely in the world around them. Simultaneously, the environment it is installed in, or traveling through, will feature multiple sensor-intensive intelligent devices. The challenge is that IoT and robotics technology are often considered separate fields. Thus the synergies across the two disciplines go unexplored. But reimagined together, IoT and industrial robotics become the Internet of Robotic Things, or IoRT.
To date, robotics and IoT have been driven by varying yet highly related objectives. IoT focuses on supporting services for pervasive sensing, monitoring, and tracking, while the robotics community focuses on production, action, interaction, and autonomous behavior. By fusing the two fields, the resulting wider-scale digital presence means intelligent sensor and data analytics are feeding better situational awareness information to robots, which means they can better execute their tasks. In short, the robots have access to more data for analysis and decision-making. Then edge computing opens the door for even more intimate collaborations between machines and between man and machine.
Simpler to Run
Paradoxically, as robots become ever more sophisticated, capable, and flexible, the effort required by end users to train them often declines. Leading manufacturers understand that shortening the learning cycle is an important means of elevating the appeal of industrial robots.
Companies like Fanuc harness AI and related technologies to accelerate teaching and learning processes. Similarly, Locus Robotics advertises warehouse robots that are so easy to train they can be deployed in just four weeks. Interfaces and tools that drive robotic learning are becoming simpler, clearer, and more efficient for end users. Such improvements are a key focus across the industry.
The cognitive capabilities of robots are already becoming indispensable as the COVID-19 pandemic revealed an urgent need to create more resilient supply chains and protect human workers. The business implications of the new intelligent systems world mean that the dynamics for decision-making in robotic systems are evolving rapidly. What we might once have seen as incremental steps now become opportunities for transformation.
To date, the robotics and IoT communities have been driven by varying, yet highly related objectives. IoT focuses on services for pervasive sensing, monitoring, and tracking, while the robotics community focuses on production action, interaction, and autonomous behavior. Fusing both fields leads to better robotics task execution. The robots have more data for analysis and AI enabled decision-making. In this way, edge computing opens the door for even closer collaboration between man and machine.
About the Author
Michel Chabroux, Senior Director, Product Management, Wind River
Michel Chabroux is responsible for the Product Management team driving technology and business strategies for Wind River’s runtime environments, including the VxWorks and Wind River Linux families of products. He has more than 20 years of industry experience including roles in technical sales, support, training and product management. Prior to joining Wind River, he was a consultant in Business Management and Information Systems working with a variety of clients. He holds a Master’s degree in Computer Science Applied to Business Administration from Universite de Lorraine.
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