Robot kinematic structure classification from time series of visual data
This work addresses the problem of robot kinematic classification for robotics and automation applications, representing an incremental improvement with specific gains in efficiency and joint type identification.
The paper tackles the robot kinematic structure identification problem by proposing a novel algorithm that identifies link sequences, joint types, and input signals from visual data time series, achieving reduced computational costs compared to state-of-the-art methods.
In this paper we present a novel algorithm to solve the robot kinematic structure identification problem. Given a time series of data, typically obtained processing a set of visual observations, the proposed approach identifies the ordered sequence of links associated to the kinematic chain, the joint type interconnecting each couple of consecutive links, and the input signal influencing the relative motion. Compared to the state of the art, the proposed algorithm has reduced computational costs, and is able to identify also the joints' type sequence.