This paper proposes a data-driven hybrid model for forecasting the battery power voltage of transportation robots by combining a wavelet method and a bidirectional deep-learning technique.In the proposed model,the on-...This paper proposes a data-driven hybrid model for forecasting the battery power voltage of transportation robots by combining a wavelet method and a bidirectional deep-learning technique.In the proposed model,the on-board battery power data is measured and transmitted.A WPD(wavelet packet decomposition)algorithm is employed to decompose the original collected non-stationary series into several relatively more stable subseries.For each subseries,a deep learning–based predictor–bidirectional long short-term memory(BiLSTM)–is constructed to forecast the battery power voltage from one step to three steps ahead.Two experiments verify the effectiveness and generalization ability of the proposed hybrid forecasting model,which shows the highest forecasting accuracy.The obtained forecasting results can be used to decide whether the robot can complete the given task or needs to be recharged,providing effective support for the safe use of transportation robots.展开更多
To address complex work conditions incredibly challenging to the stability of power line inspection robots,we design a walking mechanism and propose a variable universe fuzzy control(VUFC)method based on multi‐work c...To address complex work conditions incredibly challenging to the stability of power line inspection robots,we design a walking mechanism and propose a variable universe fuzzy control(VUFC)method based on multi‐work conditions for flying‐walking power line inspection robots(FPLIRs).The contributions of this paper are as follows:(1)A flexible pressing component is designed to improve the adaptability of the FPLIR to the ground line slope.(2)The influence of multi‐work conditions on the FPLIR's walking stability is quantified using three condition parameters(i.e.,slope,slipping degree and swing angle),and their measurement methods are proposed.(3)The VUFC method based on the condition parameters is proposed to improve the walking stability of the FPLIR.Finally,the effect of the VUFC method on walking stability of the FPLIR is teste.The experimental results show that the maximum climbing angle of the FPLIR reaches 29.1°.Compared with the constant pressing force of 30 N,the average value of slipping degree is 0.93°,increasing by 35%.The maximum and average values of robot's swing angle are reduced by 46%and 54%,respectively.By comparing with fuzzy control,the VUFC can provide a more reasonable pressing force while maintaining the walking stability of the FPLIR.The proposed walking mechanism and the VUFC method significantly improve the stability of the FPLIR,providing a reference for structural designs and stability controls of inspection robots.展开更多
基金funded by the German Federal Ministry of Education and Research Germany(FKZ:03Z1KN11,03Z1KI1)supported by the National Natural Science Foundation of China(Grant No.61873283)+3 种基金the Changsha Science&Technology Project(Grant No.KQ1707017)the Shenghua Yu-ying Talents Programme of Central South Universitythe Innovation-Driven Project of Central South Universitythe Wasion Group Limited.
文摘This paper proposes a data-driven hybrid model for forecasting the battery power voltage of transportation robots by combining a wavelet method and a bidirectional deep-learning technique.In the proposed model,the on-board battery power data is measured and transmitted.A WPD(wavelet packet decomposition)algorithm is employed to decompose the original collected non-stationary series into several relatively more stable subseries.For each subseries,a deep learning–based predictor–bidirectional long short-term memory(BiLSTM)–is constructed to forecast the battery power voltage from one step to three steps ahead.Two experiments verify the effectiveness and generalization ability of the proposed hybrid forecasting model,which shows the highest forecasting accuracy.The obtained forecasting results can be used to decide whether the robot can complete the given task or needs to be recharged,providing effective support for the safe use of transportation robots.
基金National Natural Science Foundation of China,Grant/Award Numbers:62063030,62163032Financial Science and Technology Program of the XPCC,Grant/Award Numbers:2021DB003,2022CB002‐07,2022CB011High‐level Talent Project of Shihezi University,Grant/Award Numbers:RCZK2018C31,RCZK2018C32。
文摘To address complex work conditions incredibly challenging to the stability of power line inspection robots,we design a walking mechanism and propose a variable universe fuzzy control(VUFC)method based on multi‐work conditions for flying‐walking power line inspection robots(FPLIRs).The contributions of this paper are as follows:(1)A flexible pressing component is designed to improve the adaptability of the FPLIR to the ground line slope.(2)The influence of multi‐work conditions on the FPLIR's walking stability is quantified using three condition parameters(i.e.,slope,slipping degree and swing angle),and their measurement methods are proposed.(3)The VUFC method based on the condition parameters is proposed to improve the walking stability of the FPLIR.Finally,the effect of the VUFC method on walking stability of the FPLIR is teste.The experimental results show that the maximum climbing angle of the FPLIR reaches 29.1°.Compared with the constant pressing force of 30 N,the average value of slipping degree is 0.93°,increasing by 35%.The maximum and average values of robot's swing angle are reduced by 46%and 54%,respectively.By comparing with fuzzy control,the VUFC can provide a more reasonable pressing force while maintaining the walking stability of the FPLIR.The proposed walking mechanism and the VUFC method significantly improve the stability of the FPLIR,providing a reference for structural designs and stability controls of inspection robots.
