KOROS Initiative: Automatized Throwing and Catching for Material Transportation

Pongratz, Martin; Pollhammer, Klaus; Szep, A.

doi:10.1007/978-3-642-34781-8_10

Cited by 7 publications

(9 citation statements)

References 13 publications

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“…Robotic transfer as a method of such transportation was proposed in 2006 by Frank [12]. This application was developed in [13][14][15][16][17][18][19][20][21]. Transportation of an object from some point of departure A to some destination B is as follows: the robot thrower located in A throws the object in direction B and notifies about it via the communication line, and the robot catcher located in B, having received the notification, carries out object capture on the fly.…”

Section: Introductionmentioning

confidence: 99%

“…The authors of [12,15] specify the following potential advantages of robotic throw compared to traditional conveyorbased systems:…”

Section: Introductionmentioning

confidence: 99%

“…For a successful capture, it is necessary to know in which point of space the object will be at the moment of capture, and at what speed it will move at the same time [15]. The point in space and time where the capture is carried out is selected among the set of points that the object passes when it flies through the working space of the capture device.…”

Section: Introductionmentioning

confidence: 99%

“…Their combination forms the trajectory of the object in the working space of the capture device. This ASTESJ ISSN: 2415-6698 trajectory must be predicted in advance so that the robot catcher has time to complete the capture [15]. Prediction is based on measuring the object trajectory immediately after the throw.…”

Section: Introductionmentioning

confidence: 99%

“…Prediction is based on measuring the object trajectory immediately after the throw. In general, in [15], the following four subtasks are distinguished when ensuring the transportation of objects by robotic transfer:…”

Section: Introductionmentioning

confidence: 99%

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Observing and Forecasting the Trajectory of the Thrown Body with use of Genetic Programming

Mironov¹,

Gayanov²,

Kurennov³

2019

Adv. sci. technol. eng. syst. j.

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Robotic catching of thrown objects is one of the common robotic tasks, which is explored in a number of papers. This task includes subtask of tracking and forecasting the trajectory of the thrown object. Here we propose an algorithm for estimating future trajectory based on video signal from two cameras. Most of existing implementations use deterministic trajectory prediction and several are based on machine learning. We propose a combined forecasting algorithm where the deterministic motion model for each trajectory is generated via the genetic programming algorithm. Object trajectory is extracted from video sequence by the image processing algorithm, which include Canny edge detection, Random Sample Consensus circle recognition and stereo triangulation. After that rajectory is forecasted using proposed method. Numerical experiments with real trajectories of the thrown tennis ball show that the algorithm is able to forecast the trajectory accurately.

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Section: Introductionmentioning

confidence: 99%

“…The authors of [12,15] specify the following potential advantages of robotic throw compared to traditional conveyorbased systems:…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Observing and Forecasting the Trajectory of the Thrown Body with use of Genetic Programming

Mironov¹,

Gayanov²,

Kurennov³

2019

Adv. sci. technol. eng. syst. j.

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Multi-camera tracking of mechanically thrown objects for automated in-plant logistics by cognitive robots in Industry 4.0

Qadeer,

Shah,

Sharif

et al. 2024

Vis Comput

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Employing cognitive robots, capable of throwing and catching, is a strategy aimed at expediting the logistics process within Industry 4.0’s smart manufacturing plants, specifically for the transportation of small-sized manufacturing parts. Since the flight of mechanically thrown objects is inherently unpredictable, it is crucial for the catching robot to observe the initial trajectory with utmost precision and intelligently forecast the final catching position to ensure accurate real-time grasping. This study utilizes multi-camera tracking to monitor mechanically thrown objects. It involves the creation of a 3D simulation that facilitates controlled mechanical throwing of objects within the internal logistics environment of Industry 4.0. The developed simulation empowers users to define the attributes of the thrown object and capture its trajectory using a simulated pinhole camera, which can be positioned at any desired location and orientation within the in-plant logistics environment of flexible manufacturing systems. The simulation facilitated ample experimentation to be conducted for determining the optimal camera positions for accurately observing the 3D interception positions of a flying object based on its apparent size on the camera’s sensor plane. Subsequently, a variety of calibrated multi-camera setups were experimented while placing cameras at identified optimal positions. Based on the obtained results, the most effective multi-camera configuration setup is derived. Finally, a training dataset is prepared for 3000 simulated throwing experiments where the initial part of the trajectory consists of observed interception positions, through derived best multi-camera setup, and the final part consists of actual positions. The encoder–decoder Bi-LSTM deep neural network is proposed and trained on this dataset. The trained model outperformed the current state-of-the-art by accurately predicting the final 3D catching point, achieving a mean average error of 5 mm and a root-mean-square error of 7 mm in 200 real-world test experiments.

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Transport by robotic throwing and catching: Accurate stereo tracking of the spherical object

Mironov

2017

2017 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

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KOROS Initiative: Automatized Throwing and Catching for Material Transportation

Cited by 7 publications

References 13 publications

Observing and Forecasting the Trajectory of the Thrown Body with use of Genetic Programming

Observing and Forecasting the Trajectory of the Thrown Body with use of Genetic Programming

Multi-camera tracking of mechanically thrown objects for automated in-plant logistics by cognitive robots in Industry 4.0

Transport by robotic throwing and catching: Accurate stereo tracking of the spherical object

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