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 these 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 computationally. 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.
Vallvé and Andrade-Cetto REF use a potential field computed over the entire configuration space to find exploration candidates.
Potential information fields for mobile robot exploration
Robotics Auton. Syst.
Robotics Auton. Syst.
Computer Science