Data Driven Inverse Kinematics of Soft Robots using Local Models

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Fredrik Holsten, Morten Pol Engell-norregard, Sune Darkner, Kenny Erleben

Soft robots are advantageous in terms of flexibility, safety and adaptability. It is challenging to find efficient computational approaches for planning and controlling their motion. This work takes a direct data-driven approach to learn the kinematics of the three-dimensional shape of a soft robot, by using visual markers. No prior information about the robot at hand is required. The model is oblivious to the design of the robot and type of actuation system. This allows adaptation to erroneous manufacturing. We present a highly versatile and inexpensive learning cube environment for collecting and analysing data. We prove that using multiple, lower order models of data opposed to one global, higher order model, will reduce the required data quantity, time complexity and memory complexity significantly without compromising accuracy. Further, our approach allows for embarrassingly parallelism. Yielding an overall much more simple and efficient approach.
Original languageEnglish
Title of host publication2019 International Conference on Robotics and Automation (ICRA)
PublisherIEEE
Publication date2019
Pages6251-6257
ISBN (Electronic)978-1-5386-6027-0
DOIs
Publication statusPublished - 2019
Event2019 International Conference on Robotics and Automation (ICRA) - Palais des congres de Montreal, Montreal, Canada
Duration: 20 May 201924 May 2019

Conference

Conference2019 International Conference on Robotics and Automation (ICRA)
LocationPalais des congres de Montreal
LandCanada
ByMontreal
Periode20/05/201924/05/2019

ID: 228448594