首页|Investigators from Tsinghua University Zero in on Robotics (A Sarsa Reinforcement Learning Hybrid Ensemble Method for Robotic Battery Power Forecasting)
Investigators from Tsinghua University Zero in on Robotics (A Sarsa Reinforcement Learning Hybrid Ensemble Method for Robotic Battery Power Forecasting)
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NETL
NSTL
Journal Of Central South Univ
New research on Robotics is the subject of a report. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “Building a rail transit workshop with efficient data interconnection has become an inevitable trend in the transformation and development of the current rail transit equipment industry. More and more diversified mobile transport robots have become a priority in the process of digital transformation of smart factories.” Financial supporters for this research include Beijing New Star Program of Science and Technology, China, National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Tsinghua University, “Accurate prediction of robot battery power can guide the control center to adopt scientific and reasonable instructions in advance to ensure efficient and stable operation of the logistics transportation chain. In this study, we propose a hybrid ensemble method of multiple learners based on state-action-reward-state-action (Sarsa) reinforcement learning algorithm. Maximal overlap discrete wavelet transform (MODWT) is used to preprocess the originally measured robot power supply voltage data. This significantly reduces the non-stationarity and volatility of time series data. Gated recurrent unit (GRU), deep belief network (DBN), and long short-term memory (LSTM), are utilized for the prediction modeling of subseries after decomposition. Finally, the Sarsa reinforcement learning ensemble strategy is used to weight the three basic predictors above. The performance of the Sarsa hybrid model is verified on three real mobile robot power data sets.”
BeijingPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningReinforcement LearningRobotRoboticsRobotsTsinghua University
NETL
NSTL
Journal Of Central South Univ
