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

We introduced the concept of C-space entropy recently in [9] as a measure of knowledge of Cspace 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, or 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. The first was that the sensor field of view (FOV) is a point; and the second was 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. keywords: sensor-based robot path planning, robot mounted range sensor, view planning, configuration space, configure space entropy