26,910 research outputs found
Iterative Machine Learning for Precision Trajectory Tracking with Series Elastic Actuators
When robots operate in unknown environments small errors in postions can lead
to large variations in the contact forces, especially with typical
high-impedance designs. This can potentially damage the surroundings and/or the
robot. Series elastic actuators (SEAs) are a popular way to reduce the output
impedance of a robotic arm to improve control authority over the force exerted
on the environment. However this increased control over forces with lower
impedance comes at the cost of lower positioning precision and bandwidth. This
article examines the use of an iteratively-learned feedforward command to
improve position tracking when using SEAs. Over each iteration, the output
responses of the system to the quantized inputs are used to estimate a
linearized local system models. These estimated models are obtained using a
complex-valued Gaussian Process Regression (cGPR) technique and then, used to
generate a new feedforward input command based on the previous iteration's
error. This article illustrates this iterative machine learning (IML) technique
for a two degree of freedom (2-DOF) robotic arm, and demonstrates successful
convergence of the IML approach to reduce the tracking error.Comment: 9 pages, 16 figure. Submitted to AMC Worksho
Computational neural learning formalisms for manipulator inverse kinematics
An efficient, adaptive neural learning paradigm for addressing the inverse kinematics of redundant manipulators is presented. The proposed methodology exploits the infinite local stability of terminal attractors - a new class of mathematical constructs which provide unique information processing capabilities to artificial neural systems. For robotic applications, synaptic elements of such networks can rapidly acquire the kinematic invariances embedded within the presented samples. Subsequently, joint-space configurations, required to follow arbitrary end-effector trajectories, can readily be computed. In a significant departure from prior neuromorphic learning algorithms, this methodology provides mechanisms for incorporating an in-training skew to handle kinematics and environmental constraints
- …