IoT concepts

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.

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.

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.