Spiking Networked System for Anomaly Detection in Vision-Guided Robots

In the field of robotics, ensuring reliable and efficient performance is crucial, especially when robots are entrusted with critical tasks. Anomaly detection systems play a crucial role in maintaining this reliability by detecting deviations from normal behavior and taking timely interventions. Traditional model-and knowledge-based approaches, while effective in controlled environments, reach their limits in dynamic and resource-constrained settings due to their reliance on predefined models, expert knowledge and high computational requirements. While data-driven methods, especially those using deep learning, offer better adaptability, they also bring challenges in terms of energy consumption and hardware limitations. To address these issues, this paper proposes a hybrid anomaly detection system that utilizes Spiking Neural Networks (SNNs) and Convolutional Neural Networks (CNNs). The SNN provides low-power processing of input features for anomaly detection, while the CNN efficiently extracts spatial features from high-resolution images to realize an accurate, high-performance classifier that can be used to perform an online fine-tuning of the SNN. A large dataset for robot navigation is used as a testbed. The obtained results show that this hybrid approach increases the speed and accuracy of anomaly detection while significantly reducing energy consumption, making it well-suited for applications in resource-constrained robotic systems.