Optimization of Trajectories for Machine Learning Training in Robot Accuracy Modeling

TL;DR

This paper explores efficient trajectory planning in robot phase space to optimize data collection for machine learning-based accuracy modeling, demonstrating that simple heuristic methods outperform random sampling.

Contribution

It formulates the trajectory optimization as a TSP in 6D space and compares heuristic and random sampling methods, providing insights into efficient data collection strategies.

Findings

Nearest Neighbor heuristic outperforms random sampling

Trajectory optimization reduces data collection time

Study confirms TSP complexity in robot phase space

Abstract

Recently, machine learning (ML) methods have been developed for increasing the accuracy of robot mechanisms. Complex mechanical issues such as non-linear friction, backlash, flexibility of structure transmission elements can cause these errors and they are hard to model. ML requires training data and the above mechanical phenomena are highly dependent on position of the robot in the workspace and also on its velocity, especially near zero velocity in both directions where non-linearities such as Streibek and Coulomb friction are most pronounced. It is well known that success of ML methods depends on amount of training data and it is expensive/time consuming to collect data from physical robot motion. We therefore address the problem of searching for trajectories in the 6D space of positions and velocities which collect the most information in the least amount of time. This reduces to a special case of the traveling-salesman problem in that the robot must be programmed to visit sampled points in the position-velocity phase space most efficiently. Two goals of this work are 1) Computationally study the difficulty of the TSP in this application by applying it to X, Y, Z motion in 3D space (6D phase space) and 2) assess the effectiveness of an extremely simple Nearest Neighbor search algorithm compared to random sampling of the search space. Results confirm that Nearest Neighbor heuristic searching produces significantly better trajectories than random sampling in this application.