Analysis of human arm movement for catching a moving object

Kajikawa, Shinya; Saito, Minoru; Ohba, Kohtaro; Inooka, Hikaru

doi:10.1109/icsmc.1999.825346

Cited by 9 publications

(9 citation statements)

References 7 publications

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“…On the one hand, he or she could either estimate the IP at the very beginning of the throw (as described in [13]), go there as accurately as possible and then look at the ball again to perform a corrective movement to the true interception point. Alternatively, one might move rapidly to PMV and then attempt to follow the ball in order to match its velocity, similarly to what has been observed when catching fragile objects [11]. In our experiments, this strategy may be intended to reduce the impact of the ball hitting the hand, rather of saving a fragile object.…”

Section: Minimum Jerk In 3dmentioning

confidence: 99%

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Minimum jerk for human catching movements in 3D

Fligge¹,

McIntyre²,

Smagt³

2012

2012 4th IEEE RAS &Amp; EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

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Abstract-To investigate fast human reaching movements in 3D, we asked 11 right-handed persons to catch a tennis ball while we tracked the movements of their arms. To ensure consistent trajectories of the ball, we used a catapult to throw the ball from three different positions. Tangential velocity profiles of the hand were in general bell-shaped and hand movements in 3D coincided with well known results for 2D point-to-point movements such as minimum jerk theory or the 2/3rd power law. Furthermore, two phases, consisting of fast reaching and slower fine movements at the end of hand placement could clearly be seen. The aim of this study was to find a way to generate human-like (catching) trajectories for a humanoid robot.

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Section: Minimum Jerk In 3dmentioning

confidence: 99%

“…Many experiments have investigated human ball catching, but most of them considered only catching in 2D. For example, Kajikawa et al [11] investigated differences between human hand trajectories depending on the fragility object to be caught. A robust object is caught after the catcher's hand reaches the interception point (IP).…”

Section: B Human Ball Catchingmentioning

confidence: 99%

Minimum jerk for human catching movements in 3D

Fligge¹,

McIntyre²,

Smagt³

2012

2012 4th IEEE RAS &Amp; EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

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“…This estimateû can be used in equation (9) to predict the current plant values based on the previous input and output values, that iŝ…”

Section: Parameter Estimation To Determine Massmentioning

confidence: 99%

Dynamic capture of free-moving objects

Nagendran

Crowther

Richardson

2011

Proceedings of the Institution of Mechanical Engineers, Part I:

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A new stiffness control method is proposed for robotic systems to capture free-moving objects with minimum jerk. A bell-shaped stiffness curve is shown to minimize the jerk that occurs during capture tasks. The results verify that the method can be used to successfully decelerate objects over a predefined distance, while keeping the jerk experienced during the capture within limits. Shortcomings with the method are identified from the results, and an adaptive control scheme is proposed. An adaptive controller is designed to actively interact with the object to estimate the kinetic energy during the capture and scale the amount of force being applied accordingly. Simulation results confirm that the adaptive control scheme overcomes the shortcomings in previous methods.

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“…This kind of task is frequently performed by humans in daily life; therefore there will be many cases where robots are required to do it in the future. To make robots accomplish this task as human does, it is necessary for us to know how human plans his motion and controls his arm movement [18]. So far many studies concerning with human arm movements are reported.…”

Section: Introductionmentioning

confidence: 99%

“…These models deal with voluntary arm movement between two fixed points and do not discuss dynamic motion planning for adjusting arm movement to the changes of the environment. [18] The goal of our work is to visually track a flying object throwing towards the robotic arm and send the data to the robotic arm to move for catch the ball at expected location. As we use a new low cost self developed robotic arm (figure 1) to catch a ball that is thrown by someone towards the robot.…”

Section: Introductionmentioning

confidence: 99%

Design and development of an effective ball catching robotic arm with 3DOF

Sharma

Sekhon

2012

Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology

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This paper presents a ball catching robotic arm system with 3DOF assembled from the commercially available parts. In the previous research work complex system were designed. We have designed a very simple arm and take the results of catching ball at different angles i.e we throw the ball towards robotic arm at different angles (40 o ,45 o ,50 o and 60 o ) and get the results in our analysis we observed that the proposed system given its best results when the ball is thrown towards it at 40 o . In this system, camera system is use to perceive the trajectory of the ball, the system detects the ball with the help of a fast mean shift algorithm. It calculates the shift of the mean of the identified color intensity and according to that it sends the control commands over the serial port to the robotic arm. The basic objective to catch the flying object at the expected location. After the analysis it is observed that the catcher robotic arm can catch the ball thrown to it from 5-6 meter with an average success rate of 70-75% when throw at 40 o towards the arm.

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Analysis of human arm movement for catching a moving object

Cited by 9 publications

References 7 publications

Minimum jerk for human catching movements in 3D

Minimum jerk for human catching movements in 3D

Dynamic capture of free-moving objects

Design and development of an effective ball catching robotic arm with 3DOF

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