Title: Development Of Control Systems For Robots And Drones
Authors: Abdurashidov Azizbek Salijon o'g'li
Volume: 9
Issue: 6
Pages: 77-79
Publication Date: 2025/06/28
Abstract:
The proliferation of robots and unmanned aerial vehicles (UAVs), or drones, in applications ranging from logistics to surveillance necessitates advanced control systems to ensure autonomy, precision, and efficiency. This paper proposes a hybrid control framework that integrates Proportional-Integral-Derivative (PID) controllers with machine learning (ML) algorithms to enhance navigation, obstacle avoidance, and energy optimization in dynamic environments. The system leverages sensor fusion, path-planning algorithms, and reinforcement learning (RL) to achieve robust performance. Simulation results indicate a 22% improvement in navigation accuracy, a 95% obstacle avoidance success rate, and a 17% reduction in energy consumption compared to conventional PID-based systems. This research offers a scalable solution for autonomous systems and contributes to the advancement of robotics and drone technology.