Novel method of estimating surface condition for tiny mobile robot to improve locomotion performance

Environment recognition is an effective way for a mobile robot to move across rough terrain. In particular, this makes it possible to prevent a tiny mobile robot from getting stuck or turning over. Several studies have been conducted on environment recognition using a laser range finder or camera. However, almost all of these studies focused on obstacle detection or shape recognition, which cannot be used to recognize the surface condition such as slipperiness. The purpose of this work is to design a model for estimating the surface condition using a tiny mobile robot. We set slipperiness as one of the parameters for recognizing the surface condition, which is already used by terramechanics, along with two additional parameters, the hardness and unevenness. We find that a robot can roughly estimate the ground hardness by measuring the current peak of a motor and the unevenness from measuring the robot posture. By recognizing the surface condition, the robot can change the parameters of the controlling motor based on the ground characteristics. This new method for recognizing the surface condition is significant, not only because it fills gaps in the previous research, but also because it does not require any special sensors such as a laser range finder and does not consume a large quantity of energy. Therefore, it achieves a core objective of our environmental monitoring system using multiple mobile robot.