首页|New Robotics Study Results Reported from Xi'an Jiaotong University (A Real-time Hierarchical Control Method for Safe Humanrobot Coexistence)
New Robotics Study Results Reported from Xi'an Jiaotong University (A Real-time Hierarchical Control Method for Safe Humanrobot Coexistence)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ro botics. According to news reporting originating in Xi'an, People's Republic of C hina, by NewsRx journalists, research stated, "In modern industrial environments , robots are expected to work close to human operators collaborating with them i n completing various tasks, e.g., collaborative assembly or delivery of objects. However, it is difficult to take task optimization into account under the premi se of ensuring safety in a human-involved dynamic environment." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Key R&D Program of China, China Schola rship Council. The news reporters obtained a quote from the research from Xi'an Jiaotong Univer sity, "Therefore, a real-time hierarchical control method containing two hierarc hical optimal controllers with complementary functions is proposed to tackle the above problems. The upper-layer Model Predictive Controller aims at performing primary tasks, such as end-effector pose tracking, singularity, and joint limit avoidance. It is formulated as a Bayesian Inference problem with a Gaussian proc ess prior and an exponential likelihood function. The resulting maximum a poster iori estimation problem can be solved efficiently using the Matrix- Scaled Stein Variational Gradient Descent and GPU. The upper-layer optimal controller aims at performing primary tasks, such as end-effector pose tracking, singularity, and joint limit avoidance. The lower layer safety-critical controller, formulated as a constrained quadratic programming problem, is responsible for tracking the ou tput interpolation of the higher-layer controller while respecting the safety co nstraints constructed in the form of Stochastic Control Barrier Functions. Both of the optimal controllers run repeatedly but with different frequencies (upper- layer controller: 20 Hz, lower-layer controller: 40 Hz). The proposed method pro vides a solution to deal with both collision avoidance and task constraints."
Xi'anPeople's Republic of ChinaAsiaEmerging TechnologiesMachine LearningRobotRoboticsXi'an Jiaotong Univer sity
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