Early warning method for inspection robot based on Monte Carlo positioning algorithm and grey theory
Traditional substation equipment detection methods mainly rely on human resources,but at present,in view of the cur-rent unmanned scene of substation inspection,combined with the internal environment characteristics of substation and equipment fault early warning requirements,a substation inspection robot early warning system based on Monte Carlo positioning algorithm and gray theory is proposed.In order to overcome the problems of robot kidnapping and fixed particle number in traditional Monte Carlo al-gorithm,an Adaptive Monte Carlo localization algorithm is proposed to locate the position of inspection robot in real time.In order to overcome the disadvantage that big data needs a lot of training data,the grey early warning algorithm with low data requirement is se-lected to early warn the power equipment failure.Then,a warning method of inspection robot based on Monte Carlo positioning algo-rithm and grey theory is proposed.Through the real scene path planning and insulation equipment condition monitoring experiments,it is proved that the proposed method can effectively return the inspection path and equipment condition inspection task in real time.
monte carlo positioninggray theoryinspection robotunmanned scene
万方数据
维普
