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Published Paper Details:
AI-Based Low-Latency Collision-Avoidance System for Industrial Machine
Authors
Prof. Mohite P. B.,Muskan Shabbir Shaikh,Srushti Harish Shinde,Vaibhavi Narayan Bhosale,Prof.V.A. Pandit
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Keywords
Collision avoidance, Industrial safety, Edge computing, Ultrasound sensors, Temporal Convolutional Network (TCN), Machine learning, Low-power MCU, Raspberry Pi, Real-time processing, Sensor fusion, Acoustic noise robustness, Embedded systems, Proximity sensing, Smart manufacturing, Industrial automation.
Abstract
The rise of autonomous and semi-autonomous machinery in industrial settings necessitates advanced safety mechanisms to ensure smooth operation while preventing collisions and protecting nearby workers [1]. This project proposes an AI-based Extreme-Edge Collision-Avoidance System utilizing a Temporal Convolutional Network (TCN) deployed on a Raspberry Pi microcontroller. The system integrates multiple sensing technologies, including LIDAR, Camera Modules, Ultrasonic Sensors, and Infrared Sensors, to comprehensively monitor the machine's surroundings [2]. At the system's core, the STM32 microcontroller processes real-time data from the sensors via a driver circuit, ensuring ultra-low-latency response. AI-based monitoring runs on a Raspberry Pi, analyzing time-series sensor data using the TCN model to detect potential hazards in real-time [3]. The AI algorithm predicts collision risks and enhances situational awareness, ensuring timely interventions critical for industrial safety [4]. Upon detecting an obstacle, the Raspberry Pi triggers immediate corrective actions, controlling machinery operations while engaging an alarm and alert system to notify nearby workers. The integration of Raspberry Pi extends computational flexibility, supporting data logging, visualization, and remote monitoring, while enabling machine learning model updates [4]. The system ensures robust performance in noisy and dynamic industrial environments by leveraging sensor fusion and AI. Extreme-edge processing minimizes latency, optimizes energy consumption, and maintains a compact memory footprint. Designed for seamless integration into industrial applications, this real-time collision avoidance system significantly enhances workplace safety, reduces accidents, and improves operational efficiency [5].
IJCRT's Publication Details
Unique Identification Number - IJCRT2506535
Paper ID - 289186
Author type - Indian Author
Page Number(s) - e615-e619
Pubished in - Volume 13 | Issue 6 | June 2025
DOI (Digital Object Identifier) -
No Of Downloads - 70
Author Country - India, 415003, Satara, Satara, 415003, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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Prof. Mohite P. B.,Muskan Shabbir Shaikh,Srushti Harish Shinde,Vaibhavi Narayan Bhosale,Prof.V.A. Pandit, "AI-Based Low-Latency Collision-Avoidance System for Industrial Machine", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 6, pp.e615-e619, June 2025, Available at :http://www.ijcrt.org/papers/IJCRT2506535.pdf
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