Trajectory Learning and Demonstration of a Robot Manipulator Based-on Sensor Fusion from a Hand-held Tool

This paper presents a programming system for a robot manipulator using a hand-held demonstration tool. In this system, a user holds the self-designed tool to demonstrate a desired motion path, and the robot arm replicates the learned trajectory. The path-demonstration tool, including optical tags (OT) and an IMU, is designed to collect trajectory data from hand motion. Sensor data fusion allows the system to track accurately the demonstrated trajectory. A calibration procedure is proposed to transform the trajectory from tool’s coordinate system to robot’s coordinate system. A cubic spline-based trajectory planning method is proposed to ensure smooth reproduction of the learned trajectory in both time and space. Experimental results show that the robot is able to replicate squared and curved trajectories collected from the path-demonstration tool. The average position error is within 2.435 mm in XYZ directions and the average orientation error is within 0.65 degrees.

Authors

Institutions

Publication Details

Published
2024-10-31
DOI
https://doi.org/10.1109/cacs63404.2024.10773349
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Trajectory Learning and Demonstration of a Robot Manipulator Based-on Sensor Fusion from a Hand-held Tool

Szu-Chen Ou, Kai-Tai Song
Robot Manipulation and Learning
article

Trajectory Learning and Demonstration of a Robot Manipulator Based-on Sensor Fusion from a Hand-held Tool

Szu-Chen Ou, Kai-Tai Song
article en

Abstract

This paper presents a programming system for a robot manipulator using a hand-held demonstration tool. In this system, a user holds the self-designed tool to demonstrate a desired motion path, and the robot arm replicates the learned trajectory. The path-demonstration tool, including optical tags (OT) and an IMU, is designed to collect trajectory data from hand motion. Sensor data fusion allows the system to track accurately the demonstrated trajectory. A calibration procedure is proposed to transform the trajectory from tool’s coordinate system to robot’s coordinate system. A cubic spline-based trajectory planning method is proposed to ensure smooth reproduction of the learned trajectory in both time and space. Experimental results show that the robot is able to replicate squared and curved trajectories collected from the path-demonstration tool. The average position error is within 2.435 mm in XYZ directions and the average orientation error is within 0.65 degrees.

National Yang Ming Chiao Tung University (TW)
National Science and Technology Council
Openalex Percentile: Top 74%
Robot Manipulation and Learning
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.