ROBOPILOT PI: SMART OBSTACLE AVOIDANCE AND NAVIGATION SYSTEM

Abstract

Autonomous navigation has become a key focus in intelligent transportation systems, where efficient path planning and reliable obstacle avoidance are critical to safe operation. This paper presents ROBOSENSE, an intelligent framework designed for Raspberry Pi–powered cars to achieve robust navigation in dynamic environments. The system integrates real-time sensor data, computer vision techniques, and decision-making algorithms to enable accurate detection of obstacles and adaptive path planning. By leveraging machine learning models and lightweight computation optimized for Raspberry Pi, ROBOSENSE ensures low-cost yet effective autonomous navigation. Experimental validation demonstrates that the proposed framework improves path accuracy, reduces collision risks, and enhances system responsiveness compared to conventional navigation approaches. The findings highlight ROBOSENSE as a viable solution for cost-effective autonomous vehicle prototypes and educational robotics applications