We present a decision theoretic approach to mobile robot exploration. The method evaluates the reduction of joint path and map entropy and computes a potential information field in robot configuration space using these joint entropy reduction estimates. The exploration trajectory is computed descending on the gradient of this field. The technique uses Pose SLAM as its estimation backbone. Very efficient kernel convolution mechanisms are used to evaluate entropy reduction for each sensor ray, and for each possible robot orientation, taking frontiers and obstacles into account. In the end, the computation of this field on the entire configuration space is shown to be very efficient. The approach is tested in simulations in a pair of publicly available datasets comparing favorably both in quality of estimates and in execution time against an RRT∗-based search for the nearest frontier and also against a locally optimal exploration strategy. ; This work has been supported by the Spanish Ministry of Economy and Competitiveness under Project DPI-2011-27510 and by the EU Project CargoANTs FP7-605598. ; Peer Reviewed


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    Title :

    Potential information fields for mobile robot exploration



    Publication date :

    2015-01-01



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



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