This paper introduces a pioneering hybrid framework that integrates Q-learning/deep Q-network (DQN) with a locally deployed large language model (LLM) to enhance obstacle avoidance in embedded robotic systems. The STM32WB55RG microcontroller handles real-time decision-making using sensor data, while a Raspberry Pi 5 computer runs a quantized TinyLlama LLM to dynamically refine navigation strategies. The LLM addresses traditional Q-learning limitations, such as slow convergence and poor adaptability, by analyzing action histories and optimizing decision-making policies in complex, dynamic environments. A selective triggering mechanism ensures efficient LLM intervention, minimizing computational overhead. Experimental results demonstrate significant improvements, including up to 41% higher deadlock recovery (81% vs. 40% for Q-learning + LLM), up to 34% faster time to goal (38 s vs. 58 s for Q-learning + LLM), and up to 14% lower collision rates (11% vs. 25% for Q-learning + LLM) compared to standalone Q-learning/DQN. This novel approach presents a solution for scalable, adaptive navigation in resource-constrained embedded robotics, with potential applications in logistics and healthcare.

This paper presents different methods for non-invasive validation and diagnostics of contactless devices. The radio frequency systems studied here operate at 13.56 MHz. When manufacturing these systems in volume, it is essential to separate the fully functional devices from the totally defective ones or even from those communicating but have anomalies that will lead to a significant reduction of their lifetime. This paper compares two non-invasive methods, one based on impedance measurements and the other on impulse response measurements. The advantages and drawbacks of these methods are presented and compared to their use in large-scale manufacturing. In addition to the proposed methods, this article describes two decision-making methodologies based on machine learning.This article compares also both measurement methods and machine learning tools. A robustness study shows the limitations of the employed techniques.

In order to improve behavioral analysis systems in urban environments, this paper proposes, using data extracted from video surveillance cameras, a tracking method through two approaches. The first approach consists in comparing the position of people between two images of a video and to perform tracking by proximity. The second method using Kalman filters is based on the anticipation of the position of an individual in the upcoming image. The use of this method proves to be more efficient as it allows continuing a detection even when people cross each other or when they pass behind obstacles. The use of Kalman filters in this domain provides a new approach to obtain reliable tracking and information on speed and trajectory variations. The proposed method is innovative in the way the tracking is performed and the results are exploited. Experiments were conducted in a real situation and showed that the use of some elements of the first method could be reused to integrate a notion of distance in the method based on the Kalman filter and thus improve the latter both in tracking and in detecting of abnormal behavior. This article deals with the functioning of the two methods as well as the results obtained with the same scenarios. The experimentation concludes through concrete results that the Kalman filter method is more efficient than the proximity method alone. A sample result is available online for two of the seven videos used in this article (accessed on 19 July 2021).

This paper presents a hybrid navigation architecture for mapless navigation based on monocular vision. The system combines perception-driven affordance control with event-triggered semantic reasoning within a unified decision framework. Navigation behavior is governed by interpretable perceptual signals and a vision-derived progress proxy that enables self-monitoring. A reactive control regime ensures real-time safety, while a semantic reasoning module is activated only under persistent navigation difficulty to provide structured guidance. Experimental results in simulation and real-world deployment demonstrate improved success rate, safety, and efficiency compared to reactive and continuously active semantic baselines, while maintaining real-time performance on embedded hardware.