Autonomous Navigation System Design for Intelligent Engineering Robotic Platforms

Abstract

The advancement of intelligent robotics and automation technologies has significantly increased the demand for autonomous robotic platforms capable of operating efficiently in complex and dynamic engineering environments. Conventional robotic navigation systems often rely on predefined paths and limited sensing mechanisms, which reduce operational flexibility and adaptability in real-time applications. This research presents the design of an autonomous navigation system for intelligent engineering robotic platforms to enhance mobility, environmental awareness, and decision-making capability. The proposed framework integrates advanced sensing technologies, machine vision systems, artificial intelligence algorithms, and real-time control mechanisms to enable autonomous path planning, localization, obstacle detection, and adaptive navigation. Sensor fusion techniques are employed to combine information from LiDAR sensors, cameras, ultrasonic sensors, and inertial measurement units for accurate environmental perception and positioning. Intelligent navigation algorithms and machine learning–based decision models are utilized to optimize movement trajectories, improve obstacle avoidance capability, and ensure stable robotic operation under varying environmental conditions. The system is evaluated through simulation and experimental testing using parameters such as navigation accuracy, path optimization efficiency, response time, obstacle avoidance rate, energy consumption, and operational reliability. Experimental results demonstrate that the proposed autonomous navigation framework significantly improves robotic mobility, reduces collision risk, and enhances task execution performance compared with conventional navigation approaches. The findings further reveal improved adaptability and real-time decision-making capability in unstructured and dynamic engineering environments.