IoT concepts

SIMULATION-BASED EVALUATION OF HYPERBOLIC SECANT POTENTIAL FIELD FOR REAL-TIME OBSTACLE AVOIDANCE

Obstacle avoidance is a fundamental capability for autonomous mobile robots, ensuring safe navigation in dynamic and unstructured environments. This paper presents a novel approach to real-time obstacle avoidance based on an Artificial Potential Field Method (APFM) utilizing a hyperbolic secant function. A mathematical formulation of the proposed model is developed and analyzed. To validate the approach, a simulation framework was implemented using ROS 2, the Gazebo simulator, and the TurtleBot3 Burger platform.

Enabling smart spectroscopy via Arduino IoT Cloud

This work presents a cloud-integrated IoT system for the real-time control and spectral monitoring of tunable light sources. Leveraging the Arduino IoT Cloud platform, the system has established bidirectional commu- nication via the MQTT protocol to manage individual color channels of a programmable light source. Spectral data has been captured using a StellarNet spectrometer and transmitted over to the Arduino IoT Cloud, enabling live visualization and feedback control on the cloud dashboard.

Computational evaluation of Laplace artificial potential field methods for real-time obstacle avoidance in Gazebo

the goal of this article is to present evaluation results for a proposed modification of the Artificial Potential Field Method (APFM). The mathematical model employs Laplace functions to compute repulsive fields to simplify calculations. Additionally, the study introduces a comprehensive evaluation framework using Gazebo and ROS2, designed to test various obstacle avoidance algorithms in simulated environments. Experiments have been conducted in a virtual room containing static obstacles of diverse shapes.