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

Abstract

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.

Discriminative context-aware network for camouflaged object detection

“…Similarly, ( Dong et al, 2021 ) proposed an MCIF-Net framework that integrates a large receptive field and an effective feature aggregation strategy into a unified framework to extra rich context features for accurate COD. In addition to existing literature, recent advancements, and relevant studies, such as the notable works of ( Hussain et al, 2021 ; Qadeer et al, 2022 ; Naqvi et al, 2023 ), contribute to the understanding of object detection, tracking, and recognition in various contexts, enhancing the breadth and depth of the related literature. Despite research devoted to the challenges in the field of COD to achieve out-standing performance in terms of accuracy, existing deep learning-based COD methods suffer major limitations such as weak boundaries (i.e., edges), low boundary contrast, variations in object appearances, such as object size and shape, leading to unsatisfactory segmentation performance ( Fan et al, 2020a ; Mei et al, 2021 ; Ji et al, 2022 ), and raises the demands of more advanced feature fusion strategies.…”

A novel CNN gap layer for growth prediction of palm tree plantlings

“…Many notable works have been done. The researchers in [27][28][29] simulated a 3D environment and deployed a virtual pinhole camera anywhere in the three-dimensional space surrounding the internal logistics system. In addition, they used multi-view geometry among virtual cameras to get a better look at the trajectories.…”

Advanced Intelligent Control in Robots

“…“Intelligent Tracking of Mechanically Thrown Objects by Industrial Catching Robot for Automated In-Plant Logistics 4.0” [ 4 ] aims to accelerate the transportation process and increase productivity through the optimized utilization of in-plant facilities. The authors develop a 3D simulated environment which enables users to throw objects with any mass, diameter or surface air friction properties in a controlled internal logistics environment.…”