Amin Basiri

University of Sannio, University College London

Papers

Total Citations

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About

Amin Basiri is a researcher advancing the field of autonomous mobile robotics, with a primary focus on intelligent navigation, path planning, and obstacle avoidance. His work bridges classical robotics algorithms with modern deep learning techniques, aiming to make robots more efficient and adaptable in real-world environments. In his highly cited 2023 paper, Basiri introduced a novel deep learning-based path-planning approach that integrates Convolutional Recurrent Neural Networks (CRNN) with the classic A* algorithm, demonstrating how neural architectures can enhance traditional search-based planning. Earlier, his 2017 work on improving robot navigation and obstacle avoidance using the Kinect 2.0 sensor showcased a practical, low-cost solution for real-time environmental perception. By leveraging a device originally designed for gaming, Basiri demonstrated how accessible hardware can be repurposed for sophisticated robotics tasks. With several papers accumulating citations, his contributions are gaining recognition for their practical impact on mobile robot autonomy. Basiri’s research is particularly valuable for students and engineers seeking to understand how deep learning can be integrated with established robotic systems to create smarter, more responsive machines.