TY - GEN
T1 - Servo load analysis for the classification of surface of locomotion in a modular snake-like robot
AU - Florez, Johana
AU - Calderon, Francisco
AU - Parra, Carlos
PY - 2012
Y1 - 2012
N2 - This paper shows the possibility of classifying the surface of locomotion of a modular snake-like robot only from torque and current sensors in the servo-motors. Locomotion in modular snake-like robots is made from gaits that involve the entire body structure, in this particular work we use a modular snake-like robot consisting of 16 modules located 90 degrees rotated one with respect to the previous, this robot is built from Dynamixel AX-12 servos, these servos provide load information based on torque and power consumption. This article presents an analysis from two classifiers, supervised and unsupervised for load temporary data in each of the 16 modules, for three different gaits used in the robot, linear progression, side winding and lateral rolling to make an identification of the characteristics of the surface on which the robot is moving, all without the need for other external sensors. The reported results are obtained by applying two classification techniques, the first supervised (SVM) and the second unsupervised (K-means). Is concluded that it is possible to make a classification between surfaces, knowing previously the selected gait, reaching even to 100% accurancy for certain gaits.
AB - This paper shows the possibility of classifying the surface of locomotion of a modular snake-like robot only from torque and current sensors in the servo-motors. Locomotion in modular snake-like robots is made from gaits that involve the entire body structure, in this particular work we use a modular snake-like robot consisting of 16 modules located 90 degrees rotated one with respect to the previous, this robot is built from Dynamixel AX-12 servos, these servos provide load information based on torque and power consumption. This article presents an analysis from two classifiers, supervised and unsupervised for load temporary data in each of the 16 modules, for three different gaits used in the robot, linear progression, side winding and lateral rolling to make an identification of the characteristics of the surface on which the robot is moving, all without the need for other external sensors. The reported results are obtained by applying two classification techniques, the first supervised (SVM) and the second unsupervised (K-means). Is concluded that it is possible to make a classification between surfaces, knowing previously the selected gait, reaching even to 100% accurancy for certain gaits.
KW - Classification
KW - K Means
KW - Modular snake-like robot
KW - Pattern recognition
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=84870704299&partnerID=8YFLogxK
U2 - 10.1109/STSIVA.2012.6340549
DO - 10.1109/STSIVA.2012.6340549
M3 - Conference contribution
AN - SCOPUS:84870704299
SN - 9781467327619
T3 - STSIVA 2012 - 17th Symposium of Image, Signal Processing, and Artificial Vision
SP - 13
EP - 18
BT - STSIVA 2012 - 17th Symposium of Image, Signal Processing, and Artificial Vision
T2 - 17th Symposium of Image, Signal Processing, and Artificial Vision, STSIVA 2012
Y2 - 12 September 2012 through 14 September 2012
ER -