AI-Enabled Robot Processor Design with RFID-Based Anti-Collision System

This work successfully demonstrates the design and analysis of a VLSI-based intelligent collision avoidance system using RFID-based sensing and hardware-level decision-making. The implementation using Verilog HDL and simulation through industry-standard EDA tools confirms that the system operates efficiently under different input conditions. The results show that the architecture provides fast response, accurate detection of obstacles, and reliable generation of control signals such as move, stop, and directional changes. The simulation outputs indicate that the system achieves high accuracy in decision-making with minimal latency, ensuring effective real-time performance. The hardware-based approach improves overall system reliability and deterministic behavior compared to conventional software-based methods. The modular design structure also enables easy scalability and integration into advanced autonomous systems. Although the system demonstrates efficient performance through simulation, it is currently limited to a simulation-based environment and rule-based AI logic. Future work can focus on hardware implementation using an FPGA, integration of advanced machine learning techniques for adaptive decision-making, and enhancement of sensing capabilities using additional sensors. Further optimisation can also be carried out to improve system flexibility and performance in complex real-world scenarios. Overall, the work proves that combining VLSI design with RFID-based sensing and AI-driven logic provides an effective solution for real-time collision avoidance systems, making it suitable for applications in robotics, autonomous vehicles, and industrial automation.  

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