View planning for exploration via maximal C-space entropy reduction for robot mounted range sensors

Abstract

We introduced the concept of C-space entropy recently as a measure of knowledge of configuration space (C-space) for sensor-based exploration and path planning for general robot–sensor systems. The robot plans the next sensing action to maximally reduce the expected C-space entropy, also called the Maximal expected Entropy Reduction (MER) criterion. The resulting view planning algorithms showed significant improvement of exploration rate over physical space-based criteria. However, this expected C-space entropy computation made two idealized assumptions: (i) that the sensor field of view (FOV) is a point and (ii) that there are no occlusion (or visibility) constraints, i.e., as if the sensor can sense through the obstacles. We extend the expected C-space entropy formulation where these two assumptions are relaxed, and consider a range sensor with non-zero volume FOV and occlusion constraints, thereby modeling a realistic range sensor. Planar simulations and experimental results on the SFU Eye-in-Hand system show that the new formulation results in further improvement in C-space exploration efficiency over the point FOV sensor-based MER formulation.