Enhancing Robotic Tactile Perception through Image Tactile Sensors

Abstract

This project builds upon prior work into the use of image tactile sensors to improve gripper performance through the adjustment of marker identifying algorithms and the initial construction of a physical gripping system. I focused on improving marker tracking algorithms, refining the physical design of sensor modules, and enabling real-time data processing. The previous image tactile sensing system has been enhanced through the development of new sensor designs, integration of a multi-camera system, and incorporation of a MySQL database for real-time data storage and analysis. Marker tracking accuracy and processing speed were significantly improved by introducing techniques like k-means clustering, watershed segmentation, and frame-skipping, alongside the use of diffused lighting and optimized illumination parameters. Three novel sensor modules were fabricated, including a black rubber module with manually placed markers, a clear silicone pad for small object interactions, and a gel-tip-inspired design for dynamic sensing. These modules demonstrated improved adaptability, scalability, and accuracy for tactile sensing. MySQL integration facilitated long-term data aggregation and enabled retrospective analysis of sensor performance, paving the way for machine learning applications in predictive force estimation. The database allows for the categorization of force measurements by sensor type, object size, and timestamp, enabling continuous system refinement. Real-time feedback was achieved by addressing prior computational challenges, reducing noise, and eliminating reliance on external lighting conditions. Testing with multiple gripper models validated the system's capability for precise tactile sensing in robotic manipulation tasks, allowing for accurate handling of a larger array of object sizes. The updated system supports continuous force tracking and object recognition during gripper operation. Future work will focus on miniaturizing the sensors, further optimizing image analysis algorithms, and leveraging accumulated data for enhanced system performance. This semester's advancements mark a significant step toward developing low-cost, efficient tactile sensing systems for applications in robotics, prosthetics, and human-robot interaction. Documenting these aspects of the implementation would make improving robotic gripping and navigation more accessible for a wider group of users. Because of the limitation of time, the physical calibration of the grippers and experiments regarding improvement to object manipulation performance are future considerations.