Cover cropping has emerged as a critical practice in sustainable agriculture, offering benefits including soil erosion control, nutrient cycling enhancement, and carbon sequestration. However, conventional cover crop establishment methods face significant challenges in timing, precision, and integration with cash crop management. The development of autonomous under-canopy robotic systems represents a transformative approach to precision cover crop seeding, enabling interseeding operations without disrupting standing crops. This article reviews the state-of-the-art in autonomous agricultural robots designed for under-canopy operations, focusing on system architectures, sensing technologies, navigation strategies, and precision seeding mechanisms. Key technological components include multi-modal perception systems combining LiDAR, stereo vision, and RTK-GPS for robust localization; adaptive navigation algorithms for crop row following and obstacle avoidance in cluttered environments; and pneumatic or mechanical seeding actuators with variable rate control. Applications span diverse cropping systems where robotic interseeding supports soil health improvement, weed suppression, and climate adaptation strategies. Despite promising advances, challenges remain in system robustness, energy efficiency, economic viability, and scalability for widespread farmer adoption. Future research directions emphasize enhanced autonomy through deep learning-based perception, multi-robot coordination for large-scale deployment, and integration with precision agriculture decision support systems to optimize seeding parameters based on real-time soil and crop conditions.