Omnidirectional Locomotion for Quadruped Robots
RoboCup 2001: Robot Soccer World Cup V
Evolutionary Multiobjective Design in Automotive Development
Applied Intelligence
Dynamic vehicle routing using genetic algorithms
Applied Intelligence
Autonomous Learning of Stable Quadruped Locomotion
RoboCup 2006: Robot Soccer World Cup X
Learning in a High Dimensional Space: Fast Omnidirectional Quadrupedal Locomotion
RoboCup 2006: Robot Soccer World Cup X
Machine learning for fast quadrupedal locomotion
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Machine Learning With AIBO Robots in the Four-Legged League of RoboCup
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Incremental 3D reconstruction using Bayesian learning
Applied Intelligence
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In the present paper we describe an efficient and portable optimization method for calibrating the walk parameters of a quadruped robot, and its contribution for the robot control and localization. The locomotion of a legged robot presents not only the problem of maximizing the speed, but also the problem of obtaining a precise speed response, and achieving an acceptable odometry information. In this study we use a simulated annealing algorithm for calibrating different parametric sets for different speed ranges, with the goal of avoiding discontinuities. The results are applied to the robot AIBO in the RoboCup domain. Moreover, we outline the relevance of calibration to the control, showing the improvement obtained in odometry and, as a consequence, in robot localization.