Deep Learning System for Object Detection and Collision Free Handling in Industrial Robots

Ashish Chouhan; Shivam Shandilya; Pravanjan Nayak; Debasish Mishra; Surjya Kanta Pal

doi:10.47852/bonviewAIA52023472

Authors

Ashish Chouhan Department of Electronics and Communication Engineering, Birla Institute of Technology, India
Shivam Shandilya Department of Electrical and Electronics Engineering, Birla Institute of Technology, India
Pravanjan Nayak Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, India
Debasish Mishra Department of Data Science and Information Systems, IFMR GSB, Krea University, India https://orcid.org/0000-0002-5317-3111
Surjya Kanta Pal Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, India

DOI:

https://doi.org/10.47852/bonviewAIA52023472

Keywords:

robot, recognition, classification, grasping, pose estimation, collision-free

Abstract

This article presents a deep learning system designed to detect objects on a shop floor, classify them, estimate their pose, and direct a robot to pick them up without collision in real time. This system consists of specific algorithms devised for each of these tasks and has been implemented and tested in an industrial robot. Data collection involved capturing frames of several texture-less cuboid-shaped objects using a low-cost web camera. A convolutional neural network analyzes these frames to detect the object, which is then passed to the image processing module for pose estimation, followed by a sequence scheduling algorithm to determine its grasping. The system achieves 95% accuracy for object detection and 100% for object grasping. The practicality of the solution is confirmed through testing under various conditions such as fluctuating lighting, different colors, undefined orientation, and non-uniform spacing between the objects. The explainability of the model is demonstrated by successfully testing it in these situations. Results show the solution is agnostic to object orientation and background. FurtherFurthermore, it is computationally efficient, accurate, and cost-effective, as it utilizes a webcam, minimizing the overall cost. Additionally, the efficacy of the object detection model is found to be superior when compared to state-of-the-art algorithms.

Received: 22 May 2024 | Revised: 26 May 2025 | Accepted: 24 September 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support the findings of this study are openly available in GitHub at https://github.com/AshishChouhan85/Dataset.

Author Contribution Statement

Ashish Chouhan: Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft. Shivam Shandilya: Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft. Pravanjan Nayak: Methodology, Validation, Data curation. Debasish Mishra: Methodology, Formal analysis, Investigation, Writing – review & editing, Visualization. Surjya Kanta Pal: Conceptualization, Resources, Writing – review & editing, Supervision.