An Inertia-Based Surface Identification System

In many robotics applications, knowing the material properties around a robot is often critical for the robot's successful performance. For example, in mobility, knowledge about the ground surface may determine the success of a robot's gait. In manipulation, the physical properties of an object may dictate the results of a grasping strategy. Thus, a reliable surface identification system would be invaluable for these applications. This paper presents an Inertia-Based Surface Identification System (ISIS) based on accelerometer sensor data. Using this system, a robot actively "knocks" on a surface with an accelerometer-equipped device (e.g., hand or leg), collects the accelerometer data in real-time, and then analyzes and extracts three critical physical properties, the hardness, the elasticity, and the stiffness, of the surface. A lookup table and k-nearest neighbors techniques are used to classify the surface material based on a database of previously known materials. This technique is low-cost and efficient in computation. It has been implemented on the modular and self-reconfigurable SuperBot and has achieved high accuracy (95% and 85%) in several identification experiments with real-world material.