Working safely is not only about processes but context — understanding the work environment and circumstances and being able to predict what other people will do next. A new system empowers robots with this level of context awareness, so they can work side-by-side with humans on assembly lines more efficiently and without unnecessary interruptions.
Instead of being able to only judge distance between itself and its human co-workers, the human-robot collaboration system can identify each worker as well as the worker’s skeleton model, which is an abstract of the worker’s body volume. Using this information, the context-aware robot system can recognize the worker’s pose and even predict the next pose. These abilities provide the robot with a context to be aware of while interacting.
The system operates with artificial intelligence that requires less computational power and smaller datasets than traditional machine learning methods. It relies instead on a form of machine learning called transfer learning, which reuses knowledge developed through training before being adapted into an operational model.
With a current collaborative robot, when a human approaches it, the robot slows down and if the worker comes close enough, the robot will stop. If the person moves away, it resumes. The context-aware robot system can be compared to a self-driving car that recognizes how long a stoplight has been red and anticipates moving again. Instead of braking or downshifting, it begins to adjust its speed by cruising toward the intersection, thereby sparing wear on the brakes and transmission.
Experiments showed that with context, a robot can operate safer and more efficiently without slowing down production. In one test, a robot arm’s path was blocked unexpectedly by someone’s hand. But rather than stop, the robot adjusted — it predicted the future trajectory of the hand and the robot moved its arm around the hand.
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