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Hitting the sweet spot: automatic optimization of energy transfer during tool-held hits

Vogel, Jörn and Takemura, Naohiro and Höppner, Hannes and Smagt, Patrick and Ganesh, Gowrishankar (2017) Hitting the sweet spot: automatic optimization of energy transfer during tool-held hits. In: IEEE International Conference on Robotics and Automation ICRA. IEEE. 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapure.

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Tool-held hitting tasks, like hammering a nail or striking a ball with a bat, require humans, and robots, to purposely collide and transfer momentum from their limbs to the environment. Due to the vibrational dynamics, every tool has a location where a hit is most efficient results in minimal tool vibrations, and consequently maximum energy transfer to the environment. In sports, this location is often referred to as the "sweet spot" of a bat, or racquet. Our recent neuroscience study suggests that humans optimize hits by using the jerk and torque felt at their hand. Motivated by this result, in this work we first analyze the vibrational dynamics of an end-effector-held bat to understand the signature projected by a sweet spot on the jerk and torque sensed at the end-effector. We then use this analysis to develop a controller for a robotic "baseball hitter". The controller enables the robot-hitter to iteratively adjust its swing trajectory to ensure that the contact with the ball occurs at the sweet spot of the bat. We tested the controller on the DLR LWR III manipulator with three different bats. Like a human, our robot hitter is able to optimize the energy transfer, specifically maximize the ball velocity, during hits, by using its end effector position and torque sensors, and without any prior knowledge of the shape, size or material of the held bat.

Item URL in elib:https://elib.dlr.de/113121/
Document Type:Conference or Workshop Item (Speech)
Title:Hitting the sweet spot: automatic optimization of energy transfer during tool-held hits
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Vogel, JörnJoern.Vogel (at) dlr.deUNSPECIFIED
Höppner, HannesHannes.Hoeppner (at) dlr.deUNSPECIFIED
Smagt, PatrickVolkswagenUNSPECIFIED
Ganesh, GowrishankarCNRS-AIST Joint Robotics LabUNSPECIFIED
Date:May 2017
Journal or Publication Title:IEEE International Conference on Robotics and Automation ICRA
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Keywords:Learning, Adapative Systems
Event Title:2017 IEEE International Conference on Robotics and Automation (ICRA)
Event Location:Singapure
Event Type:international Conference
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben Terrestrische Assistenz-Robotik
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Cognitive Robotics
Deposited By: Vogel, Jörn
Deposited On:17 Jul 2017 17:14
Last Modified:11 Feb 2018 11:41

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