Robotics & Machine Learning Daily News2024,Issue(Oct.31) :109-110.

Studies from Japan Aerospace Exploration Agency (JAXA) Provide New Data on Robot ics and Mechatronics (Covariance Control for Uncrewed Aircraft Systems Under Cor related Uncertainty)

Robotics & Machine Learning Daily News2024,Issue(Oct.31) :109-110.

Studies from Japan Aerospace Exploration Agency (JAXA) Provide New Data on Robot ics and Mechatronics (Covariance Control for Uncrewed Aircraft Systems Under Cor related Uncertainty)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews-Investigators discuss new findings in robotics an d mechatronics. According to news reportingout of Tokyo, Japan, by NewsRx edito rs, research stated, "Uncrewed aircraft systems and advanced airmobility are as sociated with uncertain conditions, such as wind prediction errors, and they may deviatefrom dedicated airspaces referred to as corridors."Financial supporters for this research include Japan Society For The Promotion o f Science.Our news editors obtained a quote from the research from Japan Aerospace Explora tion Agency(JAXA): "However, further studies on these aspects are required. Thi s study focuses on the covariance control for a stochastic discrete-time linear system under correlated uncertainties. A covariance controlproblem with chance constraints can be formulated as a convex-programming problem. Numerical simulations of uncrewed aircraft systems path planning can be used to compare two contr ol laws: covariancecontrol laws that consider correlated and noncorrelated nois e. Numerical simulations were conducted toverify that the covariance control la w considering correlated noise generates a trajectory that appropriatelysatisfi es the constraints. Consequently, the effect of considering correlated noise in the covariance controllaw was clarified."

Key words

Japan Aerospace Exploration Agency (JAXA )/Tokyo/Japan/Asia/Mathematics/Mechatronics/Numerical Modeling/Robotics

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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