Real-Time Autonomous Navigation with ResNet-18 and PyTorch Based on AI computer Jetson Nano

Vidangos Ponce G.R.; Quispe Barra M.A.; Lozada Vilca R.A.; Condori S.M.; Coila Monje J.I.; Arredondo Mamani J.R. · Proceedings - IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, ChileCon (2025) · DOI: 10.1109/chilecon66915.2025.11476262

Resumen

This paper presents the design and implementation of a low-cost autonomous vehicle prototype that employs machine learning (ML) and computer vision techniques. The prototype integrates a ResNet-18 convolutional neural network (CNN), trained using PyTorch and deployed on an NVIDIA Jetson Nano-a compact, GPU-accelerated computing platform based on the JetBot architecture. The objective is to demonstrate the feasibility of implementing ML algorithms on affordable embedded hardware for real-time navigation tasks such as lane detection and path following. The development workflow includes image data acquisition, model training, and validation through physical testing on a 2.44 m × 1.24 m modular track. More than 2,600 labeled images were used to train the model across five t rack s cenarios, w ith t he b est c onfiguration ob tained after 60 epochs. Experimental results show that the trained ResNet-18 model achieved up to 70% trajectory-tracking accuracy and a continuous navigation distance of 208 cm before deviation. These results confirm t hat t he J etson N ano p latform c an effectively execute CNN-based control tasks in real time. This study contributes to scalable and cost-effective autonomous vehicle research by bridging the gap between theoretical ML applications and practical embedded implementations in resource-constrained environments.

Instituciones: Universidad Nacional del Altiplano; Universidad Nacional de Juliaca