Measuring the intersection of a thrown object with a vertical plane

Barteit, D.; Frank, Heinz; Pongratz, Martin; Kupzog, Friederich

doi:10.1109/indin.2009.5195885

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…Those works involve object throws through a mechanical launcher. For example, the work in [ 29 , 30 , 31 ] used single camera on the catching side and trajectory of thrown objects was predicted by combination of two methodologies for determination of 3D positions of a thrown object during its trajectory. The first methodology was applied in early phase of trajectory and used measurement of the initial parameters (angles, position and velocity) of the thrown object.…”

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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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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“…The ability to catch a thrown object requires knowledge about the trajectory and other attributes of the motion of the object (Barteit et al, 2009), which we gather with cameras. The thrown object is a tennis ball with a velocity from 4.6 m s to 5.4 m s .…”

Section: Setup and Conceptmentioning

confidence: 99%

Parallelized Flight Path Prediction using a Graphics Processing Unit

Götzinger

Pongratz

Rahmani

et al. 2017

Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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Summarized under the term Transport-by-Throwing, robotic arms throwing objects to each other are a visionary system intended to complement the conventional, static conveyor belt. Despite much research and many novel approaches, no fully satisfactory solution to catch a ball with a robotic arm has been developed so far. A new approach based on memorized trajectories is currently being researched. This paper presents an algorithm for real-time image processing and flight prediction. Object detection and flight path prediction can be done fast enough for visual input data with a frame rate of 130 FPS (frames per second). Our experiments show that the average execution time for all necessary calculations on an NVidia GTX 560 TI platform is less than 7.7ms. The maximum times of up to 11.7ms require a small buffer for frame rates over 85 FPS. The results demonstrate that the use of a GPU (Graphics Processing Unit) considerably accelerates the entire procedure and can lead to execution rates of 3.5× to 7.2× faster than on a CPU. Prediction, which was the main focus of this research, is accelerated by a factor of 9.5 by executing the devised parallel algorithm on a GPU. Based on these results, further research could be carried out to examine the prediction system's reliability and limitations (compare (Pongratz, 2016)).

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“…Table 1 summarizes these. So far feasibility of the approach is tested with spherical objects in fact tennis ball (Barteit et al, 2009). Objects of different nature behave aerodynamically differently.…”

Section: Introductionmentioning

confidence: 99%

TRACKING PLANAR-TEXTURED OBJECTS - On the Way to Transport Objects in Packaging Industry by Throwing and Catching

Akhter¹

2012

Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods

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In manufacturing systems transportation of objects can be optimized by throwing and catching them mechanically between work stations. There is a need to track thrown objects using visual sensors. Up to now only ball-shaped objects were tracked under controlled environment, where no orientation had to be considered. This work extends the task of object tracking to cuboid textured objects considering industrial environment. Indeed, tracking objects with respect to the robotics tasks to be achieved in a not too restricted environment remains an open issue. Thus, this work deals with efficient, flexible, and robust estimation of the object's pose.

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Measuring the intersection of a thrown object with a vertical plane

Cited by 7 publications

References 21 publications

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

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

Parallelized Flight Path Prediction using a Graphics Processing Unit

TRACKING PLANAR-TEXTURED OBJECTS - On the Way to Transport Objects in Packaging Industry by Throwing and Catching

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