Design and Simulation of a Numerical Controlled Throwing Device

Frank, Heinz

doi:10.1109/ams.2008.17

Cited by 7 publications

(3 citation statements)

References 6 publications

(6 reference statements)

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“…Robotic throwing has been demonstrated in the literature using different types of robotic systems that can be classified into three main categories: throwing with specialized devices such as 1-DoF or 2-DoF (degrees of freedom) launching mechanisms [5,30,[32][33][34][35][36][37], throwing by industrial robots [3,[38][39][40][41] and throwing by humanoid robots [42,43]. In the case of industrial robots, [38] used the KUKA KR-16 robot to demonstrate accurate throwing of a tennis ball to a target located approximately 2.5 m away.…”

Section: Robotic Throwingmentioning

confidence: 99%

Bimanual Dynamic Grabbing and Tossing of Objects Onto a Moving Target

Bombile¹,

Billard

2023

Preprint

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Section: Robotic Throwingmentioning

confidence: 99%

Bimanual Dynamic Grabbing and Tossing of Objects Onto a Moving Target

Bombile¹,

Billard

2023

Preprint

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“…Heinz Frank and his team developed a prototype [ 10 ] of a mechanical throwing device responsible for throwing circular objects in production systems. Through this device, the characteristics for the acceleration of the objects can be modified only by mechanical settings.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Tracking of Mechanically Thrown Objects by Industrial Catching Robot for Automated In-Plant Logistics 4.0

Qadeer

Shah

Sharif

et al. 2022

Sensors

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Industry 4.0 smart manufacturing systems are equipped with sensors, smart machines, and intelligent robots. The automated in-plant transportation of manufacturing parts through throwing and catching robots is an attempt to accelerate the transportation process and increase productivity by the optimized utilization of in-plant facilities. Such an approach requires intelligent tracking and prediction of the final 3D catching position of thrown objects, while observing their initial flight trajectory in real-time, by catching robot in order to grasp them accurately. Due to non-deterministic nature of such mechanically thrown objects’ flight, accurate prediction of their complete trajectory is only possible if we accurately observe initial trajectory as well as intelligently predict remaining trajectory. The thrown objects in industry can be of any shape but detecting and accurately predicting interception positions of any shape object is an extremely challenging problem that needs to be solved step by step. In this research work, we only considered spherical shape objects as their3D central position can be easily determined. Our work comprised of development of a 3D simulated environment which enabled us to throw object of any mass, diameter, or surface air friction properties in a controlled internal logistics environment. It also enabled us to throw object with any initial velocity and observe its trajectory by placing a simulated pinhole camera at any place within 3D vicinity of internal logistics. We also employed multi-view geometry among simulated cameras in order to observe trajectories more accurately. Hence, it provided us an ample opportunity of precise experimentation in order to create enormous dataset of thrown object trajectories to train an encoder-decoder bidirectional LSTM deep neural network. The trained neural network has given the best results for accurately predicting trajectory of thrown objects in real time.

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“…Other researchers have used the subject of throwing for robot's arms [1] [2], hopping [3] or as a new method for transportation of objects [4] [5].…”

Section: Introductionmentioning

confidence: 99%

Optimal passive dynamics for physical interaction: Throwing a mass

Vejdani

Hurst

2013

2013 IEEE International Conference on Robotics and Automation

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The passive dynamics of actuators may impose serious limitations to the performance of a system. Existence of inertia for example makes it impossible for the actuators to react immediately. A throwing mechanism (with electric motors) is composed of two inertias (object and motor) that decreases the performance of the system and can not be overcome with software control. But, we can use other elements (like a spring) to make the motor inertia a benefit to improve the performance of the system. Moreover, when the object is directly connected to the motor, the maximum velocity that the object can achieve is limited to the maximum velocity that can be provided by the motor. Previous research shows that passive dynamics is not always harmful, and can increase the performance of a mechanism. Here, we will extract mathematical formula that gives us the required optimum value for stiffness and/or damping of the system to give us the optimal performance given physical limitations.

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Design and Simulation of a Numerical Controlled Throwing Device

Cited by 7 publications

References 6 publications

Bimanual Dynamic Grabbing and Tossing of Objects Onto a Moving Target

Bimanual Dynamic Grabbing and Tossing of Objects Onto a Moving Target

Intelligent Tracking of Mechanically Thrown Objects by Industrial Catching Robot for Automated In-Plant Logistics 4.0

Optimal passive dynamics for physical interaction: Throwing a mass

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