IIoT is the future of workplace safety (Authentise Weekly News-In-Review – #118)

The increasingly connected systems in place at the factory floors are enabling safer workplaces. The most straightforward approach here is to don workers with sensors that can detect hazardous environment parameters like air quality and temperature or even automatically alert someone if they are injured. In most cases, however, it’s about bypassing the need for human workers to do dangerous tasks. Technologies like predictive analytics can do tremendous work in alerting supervisors before parts get broken and become hazards. Similar smart systems need to be put in place when cooperative robotics start working alongside human counterparts. Using machine learning and computer vision, safety can be guaranteed as robots can have comprehensive knowledge of their surroundings and predict human actions, as well as maximizing the robot’s effectiveness.

Using IIoT-Connected Devices for Worker Health & Safety

IBM announced collaborations with Garmin, Guardhat, Mitsufuji and SmartCone to help organizations monitor their workers’ safety with Watson IoT. Source: IBM

Workplace safety is important in any field. For example, in my line of work, I’m always vigilant of dangers from hot coffee, eye strain, or paper cuts. But in industrial environments such as the manufacturing, petrochemical, or mining industries, the potential dangers are more severe. That’s why researchers and engineers are exploring new ways to use industry 4.0 technology to protect the health and safety of industrial workers.

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How IoT and Computer Vision Can Enhance Industrial Safety

Welder's safety is protected by IoT

Using IoT sensors can feed the algorithm with real-time data and allow it to make decisions on the spot. For example, if sensors detect gas leakage, increased temperatures or unwanted humidity, work can stop at once or at the very least inform the floor manager. These type of decisions are deterministic and don’t provide much insight into the future. Another way of creating a safer environment is to use the power of computers and machine learning. By creating different scenarios, the algorithm can sense the difference between what is safe and what is not.

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Collaborative Robots Learn to Collaborate

An automated mobile robot (AMR) uses 3d vision and machine learning to navigate in a more natural manner past a person moving a cart in a warehouse aisle.

To be truly collaborative, robots must be capable of more than working safely alongside human beings. Russell Toris, director of robotics at Fetch Robotics, says robots also need to act (and “think”) more like people. This is particularly true of autonomous mobile robots (AMRs) like those manufactured by Fetch. Typically employed for material transport and data collection (such as counting inventory), these wheeled systems use vision sensors and navigation software to dynamically adapt to new environments and situations. Increasingly common in warehouses and distribution centers, this technology is likely to spread to other applications and industries, including our own.

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