In another, researchers tested whether the robot would take a tumble if it was periodically pushed with enough force to alter its direction. One trial focused on studying the robot's ability to move while performing tasks at different gaits, such as walking backward or stepping in place. And because many of the algorithms the team used stemmed from previous robotics experiments, they were able to design multiple scenarios for the simulations to run. Though various factors can be used to characterize a robot's overall safety performance, this study analyzed a set of conditions under which the robot would not fall over while actively navigating a new environment. The research, which was partly inspired by Weng's work as a vehicle safety researcher at the Transportation Research Center, which partners with the National Highway Traffic Safety Administration, takes advantage of sample-based machine learning algorithms to discern how simulated robots would fail during real-world testing. "So having safety and testing regulations in place is extremely important for the success of this kind of product." "In the future, these robots might have the chance to live with human beings side-by-side, and will most likely be collaboratively produced by multiple international parties," he said. This study develops the first data-driven, scenario-based safety testing framework of its kind for legged robots, said Weng. While there are currently some safety specifications in place for the deployment of legged robots, Weng noted that there isn't yet any common agreement on how to test them in the field. "Testing is really about assessing risk, and our aim is to investigate how much risk robotics currently presents to users or customers while in a working condition," he said. Legged robots especially, which are often made of metal and can run as fast as 20 mph, could quickly become safety hazards when expected to operate alongside humans in real and often unpredictable environments, said Weng. "It means you can't rely on the robot's ability to know how to react in certain situations, so the completeness of the testing becomes even more important."Īs mobile robots evolve to carry out more diverse and sophisticated tasks, many in the scientific community also note that the industry needs a set of universal safety testing regulations, especially as robots and other artificial intelligence have gradually begun to flow into our everyday lives. "Our work reveals that these robotic systems are complex and, more importantly, anti-intuitive," said Bowen Weng, a PhD student in electrical and computer engineering at Ohio State. The study found that many current legged robotic models don't always act predictably in response to real-life situations, meaning it's hard to predict whether they'll fail - or succeed - at any given task that requires movement. Led by a team of researchers at The Ohio State University, the study published recently in the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2022 describes a framework for testing and characterizing the safety of legged robots, machines that, unlike their wheeled counterparts, rely on mechanical limbs for movement.
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