Autonomous Navigation and Control System Design for Intelligent Engineering Robotic Platforms

Abstract

The rapid advancement of intelligent automation technologies has increased the demand for autonomous robotic platforms capable of performing complex engineering tasks with high precision, adaptability, and operational efficiency. Conventional robotic systems often depend on manual control and limited sensing capabilities, which restrict their performance in dynamic and unstructured environments. This research presents the design and development of an autonomous navigation and control system for intelligent engineering robotic platforms to enhance mobility, decision-making, and task execution capabilities. The proposed framework integrates advanced sensors, machine vision, artificial intelligence algorithms, and real-time control mechanisms to enable autonomous path planning, obstacle detection, localization, and adaptive motion control. Sensor fusion techniques are employed to combine data from LiDAR, ultrasonic sensors, inertial measurement units, and camera systems for accurate environmental perception and navigation. Intelligent control algorithms and machine learning-based decision models are utilized to optimize movement trajectories, improve navigation accuracy, and ensure stable robotic operation under varying environmental conditions. The system is evaluated through simulation and experimental testing using parameters such as navigation accuracy, obstacle avoidance efficiency, response time, path optimization, energy consumption, and operational reliability. Experimental results demonstrate that the proposed autonomous control framework significantly improves robotic navigation performance, reduces collision risk, and enhances task completion efficiency compared with conventional control approaches.