首页|Findings in Robotics Reported from University of Oxford (Planning Under Uncertai nty for Safe Robot Exploration Using Gaussian Process Prediction)
Findings in Robotics Reported from University of Oxford (Planning Under Uncertai nty for Safe Robot Exploration Using Gaussian Process Prediction)
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2024 OCT 08 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Robotics have been publi shed. According to news originating from Oxford, United Kingdom, by NewsRx corre spondents, research stated, "The exploration of new environments is a crucial ch allenge for mobile robots. This task becomes even more complex with the added re quirement of ensuring safety." Financial supporters for this research include Engineering & Physi cal Sciences Research Council (EPSRC), UK Research & Innovation (U KRI), Amazon Web Services. Our news journalists obtained a quote from the research from the University of O xford, "Here, safety refers to the robot staying in regions where the values of certain environmental conditions (such as terrain steepness or radiation levels) are within a predefined threshold. We consider two types of safe exploration pr oblems. First, the robot has a map of its workspace, but the values of the envir onmental features relevant to safety are unknown beforehand and must be explored . Second, both the map and the environmental features are unknown, and the robot must build a map whilst remaining safe. Our proposed framework uses a Gaussian process to predict the value of the environmental features in unvisited regions. We then build a Markov decision process that integrates the Gaussian process pr edictions with the transition probabilities of the environmental model. The Mark ov decision process is then incorporated into an exploration algorithm that deci des which new region of the environment to explore based on information value, p redicted safety, and distance from the current position of the robot."
OxfordUnited KingdomEuropeEmerging TechnologiesGaussian ProcessesMachine LearningMarkov Decision ProcessesRobotRoboticsUniversity of Oxford
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