Small-scale simulator on common warehouse components for low-power object detection methods

Matthieu Desmarescaux; Wissam Kaddah; Ayman Alfalou; Nicolas Picquerey

doi:10.1117/12.3013417

7 June 2024 Small-scale simulator on common warehouse components for low-power object detection methods

Matthieu Desmarescaux, Wissam Kaddah, Ayman Alfalou, Nicolas Picquerey

Proceedings Volume 13040, Pattern Recognition and Prediction XXXV; 130400P (2024) https://doi.org/10.1117/12.3013417
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

When building or renovating a warehouse, a brainstorming phase is required to discuss robotic automation. Indeed, in order to achieve optimal performances, enhancements to the goods selection processes are continually sought. This selection uses important information based on moving products. Recently, several new methods have been emerged and the time to try them still limited. To evaluate the performance of these methods, it is necessary to carry out some tests. In this paper, we introduce a small-scale simulator designed to facilitate the testing of innovations outlined in the literature. Like a real warehouse, we have a conveyor belt to simulate the movement of goods and the robotic arm proposed by the Ned2. This research presents, with limited resources, the performance of a novel method in object detection. The simulation operates autonomously and is controlled by an NVIDIA Jetson Nano card, which incorporates novel deep-Learning methods. Furthermore, a depth camera is integrated to determine the 3D position of the goods.

Citation Download Citation

Matthieu Desmarescaux, Wissam Kaddah, Ayman Alfalou, and Nicolas Picquerey "Small-scale simulator on common warehouse components for low-power object detection methods", Proc. SPIE 13040, Pattern Recognition and Prediction XXXV, 130400P (7 June 2024); https://doi.org/10.1117/12.3013417

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