Observations from RoboBusiness 2018

By Ed O'Brien

Category:
Industry Trends

Innovations in Industrial Robotics and Drones Highlight Opportunities in Digital Transformation at RoboBusiness 2018  

The recent RoboBusiness 2018 robotics conference showcased a wide variety of new and innovative industrial robotics and drone systems.  Sponsors and presenters from such organizations as ABB Robotics, Future Robotix, Harmonic Drive, MITRE, Stanford UniversityULC Robotics, Universal Robots, and Vecna Robotics, among others, attended the conference.  These organizations shared their capabilities in industrial automation, logistics, military, and medical and surgical applications.

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An Articulating Robot Arm at RoboBusiness 2018

During the conference, I had the pleasure of co-presenting a workshop and moderating a fireside chat panel discussion on several digital transformation topics.  The former was on big data and analytics in robotics, and the latter was on AI and machine learning in robotics.  Deep insights were provided by data and analytics experts from Oneida Nation Enterprises, SAS, and Stanford University, who shared their experiences and observations in data management, analytics, deep learning, machine learning, and AI. 

Workshop presenters discussed expanded opportunities available for AI and robotics in manufacturing, and how new data strategies are being used to help improve organizational efficiencies and effectiveness.  A key takeaway is a realization that managing all this data is critically important to avoid a “drowning in data” scenario for operations personnel. 

Key discussion points during the session focused on the need for a coordinated data management strategy, including the need for basic (and not-so-basic) data management tasks.  Central to this strategy is the need for master data management, data cleansing, de-duping, AI, machine learning, and predictive analytics, which are becoming critically important to the success of today’s automated industrial organizations.   

Another key takeaway from these workshop discussions included the need to manage data at the edge of the enterprise, particularly with sensor and robotics systems data.  Also important is a deeper understanding about managing the wide variety of data types present in today’s organizations, including both structured and unstructured data.

In the fireside chat panel discussion, speakers from SAS and Stanford University shared their observations about how deep learning, machine learning, and AI are helping to transform the robotics industry.  Discussions included how far AI technology has come, and how far it needs to go.  The overarching theme of this session was on how AI and robotics can amplify the capabilities of people, products, and manufacturing organizations.

Additional takeaways included an affirmation that we have come a long way in analytics and AI and machine learning in manufacturing and robotics, but we are still at the beginning of our journey.  Because of this, organizations need to be mindful of the need for foundational elements like master data management, data cleansing, and synchronization to provide a framework for improved IT and OT performance.  This leads to a better understanding of the stories being told through the underlying data, whether it be in structured or unstructured data formats.   

Consequently, such seemingly mundane tasks as data cleansing and de-duping are critical to the ongoing success of data-driven initiatives.  From there, machine learning and AI systems can then be finely-tuned to their specific applications. 

Discussions also included thoughts about factors driving the next wave of robotics through AI, the journey of robotics and AI, the need for robotics champion in organizations, and likely new technology applications for the next generations of robots.

Further takeaways include a realization that, there still needs to be a combination of human and machine insight -- a combination of both touch and technology -- to optimize the performance of production and assembly lines ad robotics systems.  This, coupled with focused strategy on data management, can help manufacturing and robotics organizations better understand and manage complex intelligent systems.  Also, organizations should look critically at the hype and reality of AI, knowing that there are ample market opportunities for AI and machine learning in manufacturing and robotics. 

In conclusion, the RoboBusiness exposition hall was filled with many interesting and innovative product demonstrations and success stories on existing and future robots.  Presentations highlighted the many opportunities to expand the use of robotics in manufacturing and logistics.  The conference was definitely informative and insightful, and an event to add to the shortlist for next year’s conference calendar.  

 

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