Surface identification using simple contact dynamics for mobile robots

This paper describes an approach to surface identification in the context of mobile robotics, applicable to supervised and unsupervised learning. The identification is based on analyzing the tip acceleration patterns induced in a metallic rod, dragged along a surface that is to be identified. Eight features in time and frequency domains are used for classification. Results show that for ten type of indoor and outdoor surfaces, reliable identification can be achieved (90. 0 and 94. 6 percent for a 1 and 4 seconds time-window, respectively), using a non-sophisticated classifier (artificial neural network). Demonstration is done on how such a sensor and a simple control strategy can be used to guide a blind robot, using a simulation and a real differential drive robot.

Authors

• Philippe Giguère
• Gregory Dudek

Keywords

• Mobile robots
• Robot sensing systems
• Acceleration
• Tactile sensors
• Accelerometers
• Vehicles
• Robotics and automation
• Data mining
• Transducers
• Vibration measurement
• Mobile Robot
• Dynamic Contact
• Neural Network
• Artificial Neural Network
• Unsupervised Learning
• Characteristic Time
• Indoor Surfaces
• Simple Control Strategy
• Training Data
• Training Dataset
• Whisker
• Sensory Signals
• Classifier Training
• Tactile Sensor
• Sensor Readings
• Spectral Phase