AI-Driven Obstacle Avoidance Algorithms in Unmanned Ground Systems

Abstract

Obstacle avoidance is a critical function in Unmanned Ground Systems (UGS), enabling safe navigation in complex and dynamic environments. This paper explores the development of AI-driven obstacle avoidance algorithms that enhance the decision-making capabilities of UGS. By leveraging machine learning and computer vision techniques, we present a real-time obstacle detection and avoidance framework that adapts to diverse terrains and environmental conditions. Our approach integrates deep learning models for visual perception, fused with data from LIDAR and ultrasonic sensors, to achieve high accuracy in obstacle recognition and path adjustment. The proposed system is capable of predicting potential collisions and generating optimized paths, even in cluttered or GPS-denied environments. Performance evaluations through simulation and field experiments demonstrate significant improvements in the UGS’s ability to navigate autonomously while avoiding static and dynamic obstacles. The results showcase the robustness, adaptability, and scalability of AI-driven obstacle avoidance algorithms, making them suitable for a wide range of UGS applications, including military, agricultural, and industrial operations.