FPGA Design for Multimodal Sensor Data Fusion in Autonomous Robots

This research study introduces a novel Field Programmable Gate Array (FPGA) design for autonomous robotics that integrates data from multiple sensors to improve their operational efficiency and decision-making. The proposed technique accomplishes real-time performance by integrating information from multiple sensors, such as LiDAR, cameras, and inertial measurement units (IMUs), using the parallel processing capabilities of FPGAs. Sensor interfaces, control logic, and complex data integration algorithms are integrated into the design. Additionally, the design is adaptable and can be implemented with FPGAs. By employing Kalman filters for state prediction and decision trees for contextual classification, the design enhances accuracy and significantly reduces latency. According to the testing results, the FPGA-based system outperforms earlier existing systems in terms of processing speed and accuracy, capable of processing up to 2000 data points per second with a latency of 10ms. This research enhances autonomous robotics by delineating a comprehensive approach to the efficient integration of sensors and the processing of data in dynamic environments.