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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Robotics and Control (JRC)
Aulia Siti Aisjah
Institut Teknologi Sepuluh Nopember Surabaya
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Detecting road damage utilizing retinanet and mobilenet models on edge devices Mahmudah, Haniah; Aisjah, Aulia Siti; Arifin, Syamsul; Prastyanto, Catur Arif
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1430-1440

Abstract

A particular form of road digitalization produces a system that detects road damage automatically and in real time, employing the device to detect road damage as an edge device. The application of RetinaNet152 and MobileNetV2 models for road damage detection on edge devices necessitates a trade-off between high system performance and efficiency. Currently, edge devices have limited storage. In this paper, we explore how tuning hyperparameters with batch size and several optimizers improves system performance on RetinaNet152 and MobileNet models, as well as how they are implemented on edge devices. After tuning hyperparameters in the batch size of the optimizer, the Adam optimizer displayed enhanced performance with mean average precision (mAP), average recall (AR), and F1-score. This implies a positive impact on overall model performance. The MobileNetV2 model's hyperparameter tuning technique significantly improves performance, resulting in faster inference times and overall system performance. This demonstrates that the MobileNetV2 model could be used directly on edge devices to identify road damage. However, the RetinaNet152 model has a lower inference time, which cannot be deployed directly to edge devices. The RetinaNet152 model can be deployed on edge devices; however, a technique for speeding up inference time is essential.
Q-RCR: A Modular Framework for Collision-Free Multi-Package Transfer on Four-Wheeled Omnidirectional Conveyor Systems Kautsar, Syamsiar; Aisjah, Aulia Siti; Arifin, Syamsul; Syai'in, Mat
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26050

Abstract

Modern logistics systems increasingly require high flexibility in handling simultaneous package transfers in compact, dynamic environments without collisions. Improper handling of multi-package transfers in omnidirectional conveyor systems can lead to deadlocks, congestion, or delivery delays, particularly in grid-based environments where routing complexity increases with package variability and layout density. This research addresses these challenges by introducing Q-RCR, a modular Q-Learning-based framework with Rule-Based Conflict Resolution (RCR) for intelligent path planning and collision handling in Four-Wheeled Omnidirectional Cellular Conveyor (FOCC) systems. The research contribution is decoupling path learning and collision handling, enabling independent agent training while minimizing computational burden and improving convergence in multi-agent scenarios. The proposed Q-RCR framework integrates Q-Learning for route optimization with a rule-based conflict resolution module, applying four adaptive strategies: Sequential Transfer, Insert Path, Reroute, and Hybrid. The method is implemented in a grid-based FOCC environment, supporting eight-directional movement and handling various package sizes. Experiments were conducted in four scenarios with grid dimensions ranging from 8×11 to 12×12 and involving up to four simultaneous packages. Results show that Q-RCR consistently outperforms Double Q-Learning, RRT, and A* regarding delivery time, path smoothness, and the number of activated cells. The hybrid mode demonstrated the most effectiveness in handling frequent collisions and maintaining operational flow continuity. The proposed framework demonstrates strong adaptability, scalability, and responsiveness, offering a practical and intelligent solution for real-time multi-package coordination in flexible manufacturing and warehouse automation environments.
Co-Authors Catur Arif Prastyanto, Catur Arif Kautsar, Syamsiar Mahmudah, Haniah Syai'in, Mat Syamsul Arifin