Dynamic motion modelling for legged robots

Abstract

An accurate motion model is an important component in modern-day robotic systems, but building such a model for a complex system often requires an appreciable amount of manual effort. In this paper we present a motion model representation, the Dynamic Gaussian Mixture Model (DGMM), that alleviates the need to manually design the form of a motion model, and provides a direct means of incorporating auxiliary sensory data into the model. This representation and its accompanying algorithms are validated experimentally using an 8-legged kinematically complex robot, as well as a standard benchmark dataset. The presented method not only learns the robot's motion model, but also improves the model's accuracy by incorporating information about the terrain surrounding the robot.978-1-4244-3804-4/09/$25.00 ©2009 IEEE

FourByThree: Imagine humans and robots working hand in hand

“…Furthermore, the model of the spring deflection and its relative output torque is required for being able to control the actuator torque. The torque-spring deflection is thus modeled by using joint probability densities that are represented by a dynamic Gaussian mixture model (DGMM) [8]. Initial experiments are being carried out to validate the results.…”

Design, Modelling and Control of Novel Series-Elastic Actuators for Industrial Robots

“…A Gaussian mixture model (GMM) is a parametric probability model represented as a finite number of weighted Gaussian distributions, which is widely used for processing multivariate data due to its high-efficiency and flexibility. In this case, a dynamic Gaussian mixture model (DGMM) is used to represent the spring model (Equation (5)), which was firstly developed by [34] for modelling dynamic motion of a legged robots and then has been further developed to model the coil-spring system of an elastic actuator in our previous study [35]. Since the number of Gaussian components can vary to enable the model to optimally fit the system, the trained DGMM model is compact to be used in real-time control.…”

Modeling and Robust Iterative Learning Control of a Quadruped Robot

“…Controlling and maintaining the stability of quadruped robots during its walking is one of the challenging problems in robotic [3], [4], [5]. Although the problem of gait generation and control of the quadruped robots have been considered increasingly by many researchers [11], [12], but the problem of modeling of the quadrupeds is not considered so much [13].…”