THE CRITERION FOR FEATURE INFORMATIVENESS ESTIMATION IN MULTI ROBOT TEAMS CONTROL
Context. The task of automation of feature set informativeness estimation process in multi robot teams control is solved. The object of the research is the process of multi robot teams control. The subject of the research is the criterion of feature set informativeness estimation.
Objective. The research objective is to develop the criterion for feature set informativeness estimation in multi robot teams control.
Method. The criterion for feature set informativeness estimation is proposed. The developed criterion is based on the idea that feature set informativeness is computed according to values of the prior probabilitіes of finding features in the descriptions of the environment states. The use of the proposed criterion allows to efficiently solve the problem of feature set informativeness estimation, leading to effective solution of the multi robots control task. The developed criterion is based on the maximizing mutual information criterion and can be applicable when measurements are interdepended and environment has a variable number of states. The criterion doesn’t require to construct models based on the estimated feature combinations, in such a way considerably reducing time and computing costs for multi robot teams control. Application of the proposed criterion for feature set informativeness estimation allows to make a decision how much a new observation will increase the certainty of the robots’ beliefs about the environment state which is observed. Results. The software which implements the proposed criterion for feature set informativeness estimation and allows to manage multi robot teams has been developed.
Conclusions. The conducted experiments have confirmed operability of the proposed criterion for feature set informativeness estimation and allow to recommend it for multi robot teams control in practice. The prospects for further researches may include the modification of the known multi robot teams control methods and the development of new ones based on the proposed criterion for feature set informativeness estimation.
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