C-space exploration using noisy sensor models

The concept of C-space entropy as a measure of knowledge of C-space for sensor-based path planning and exploration for general robot-sensor systems was recently introduced in [4]. 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 expected C-space entropy computation, however, made an idealized assumption. The sensor was assumed to measure exact data, i.e., it was not subject to noise. In this paper we extend this approach by using a real noisy sensor model. Sensing actions can then be compared on the basis of their uncertainty models. This offers the ability for using more than one principle sensor (multisensory exploration), because sensor readings can be weighted by evaluating the expected measurement quality. Additionally, it makes robot motion planning viable for tasks such as object surface inspection, which require the robot to come very close to the obstacles to achieve high sensing accuracy.