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Sökning: WFRF:(Wang Xiaoliang) > Konferensbidrag

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1.
  • Huang, Zhifeng, et al. (författare)
  • Three-dimensional posture optimization for biped robot stepping over large ditch based on a ducted-fan propulsion system
  • 2020
  • Ingår i: IEEE International Conference on Intelligent Robots and Systems. - 2153-0858 .- 2153-0866. ; , s. 3591-3597
  • Konferensbidrag (refereegranskat)abstract
    • The recent progress of an ongoing project utilizing a ducted-fan propulsion system to improve a humanoid robot's ability to step over large ditches is reported. A novel method (GAS) based on the genetic algorithm with smoothness constraint can effectively minimize the thrust by optimizing the robot's posture during 3D stepping. The significant advantage of the method is that it can realize the continuity and smoothness of the thrust and pelvis trajectories. The method enables the landing point of the robot's swing foot to be not only in the forward but also in a side direction. The methods were evaluated by simulation and by being applied on a prototype robot, JetHR1. By keeping a quasistatic balance, the robot could step over a ditch with a span of 450 mm (as much as 97% of the length of the robot's leg) in 3D stepping.
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2.
  • Wang, Hao Luo, et al. (författare)
  • A Data-driven Survival Modelling Approach for Predictive Maintenance of Battery Electric Trucks
  • 2023
  • Konferensbidrag (refereegranskat)abstract
    • Predictive Maintenance (PdM) aims to estimate the optimal moment when the maintenance of an industrial asset should be performed according to its actual health status. The goal is to minimize the costs, by finding the optimal point where the sum of the prevention and repair cost is at the lowest. Data-driven model may predict whether an asset is close to a real breakdown, therefore helping to build more cost-efficient maintenance strategies. This paper focuses on survival analysis based predictive maintenance applied to the operation of Battery Electric Trucks (BET). Cox Proportional Hazards and Random Survival Forests methods are adopted for modelling time-to-failure and the associated survival functions. Detailed telematics data from BET vehicles in real operations are used for modelling and analysis. The model performance is further improved by the feature selection and hyperparameter tuning processes.
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