Teleoperated object handling in hazardous environments poses a risk to operators. The development of drones and robotic systems opens horizons for novel civic applications such as technical inspections, maintenance at high altitudes, special deliveries or interventions in hard-to-reach areas. Previous studies have shown that using a live 3D interface along with safe communication methods, and a modular design can make interventions not only safer, but also more efficient and secure. However, easily accessible and practical prototypes are still needed to demonstrate that these solutions can be implemented at low cost.

This work aims to develop and design a remotely controlled robotic arm. It can be installed on aerial work platforms, including drones. It is a scalable and modular solution that is mitigating the operator risks. Its applicability covers the military and the civilian fields, including operations related to maintenance, inspections, light object delivery, etc. So, our goal is to design, develop and control a modular and simple hardware-software system consisting only of Arduino, servomotors, serial communication/API and a 3D interface which provide accurate control, real-time feedback and operational reliability across such diverse missions.

This paper presents a technology-enhanced, project-based educational framework for teaching intelligent control concepts in engineering curricula. The framework integrates theoretical instruction with computational model-ing and real-time experimentation using a low-cost DC motor platform, ena-bling students to follow the complete control workflow from modeling and simulation to hardware implementation and performance evaluation under realistic non-ideal conditions.

The approach is supported by interactive modeling workflows, MATLAB/Simulink tools, and Live Scripts, and is applied to control prob-lems including proportional-integral-derivative, model predictive, and model reference adaptive control. Representative laboratory case studies illustrate how students implement and experimentally validate their designs on DC motor-based systems and related platforms.

Experimental outcomes and student feedback indicate that the proposed framework improves student engagement and supports the development of practical control skills and conceptual understanding. Overall, the framework provides an accessible and affordable model for intelligent control education, bridging theory and practice through hands-on learning.

Autonomous vehicles, whether mobile (Autonomous Mobile Robots, AMRs) or aerial (Unmanned Aerial Vehicles, UAVs) are extensively studied for various objectives such as inspection, search and rescue, transportation or any other tasks related to human support. Considering that UAVs [1], as well as AMRs become essential devices used for example for delivering packages or mails, and potentially in military applications, it is interesting to evaluate adversarial interactions between them. First, it becomes relevant to evaluate if a drone or a mobile vehicle transporting a package makes the delivery to the correct recipient, and not to another one belonging to a competing delivery company. Second, it is essential to investigate the adversarial interactions between autonomous vehicles applications. In this context, it is mandatory to understand adversarial interactions following game strategies like pursuer-evader dynamics or teams-based games inspired by example like soccer.