A Model that Predicts the Material Recognition Performance of Thermal Tactile Sensing

Tactile sensing can enable a robot to infer properties of its surroundings, such as the material of an object. Heat transfer based sensing can be used for material recognition due to differences in the thermal properties of materials. While data-driven methods have shown promise for this recognition problem, many factors can influence performance, including sensor noise, the initial temperatures of the sensor and the object, the thermal effusivities of the materials, and the duration of contact. We present a physics-based mathematical model that predicts material recognition performance given these factors. Our model uses semi-infinite solids and a statistical method to calculate an F1 score for the binary material recognition. We evaluated our method using simulated contact with 69 materials and data collected by a real robot with 12 materials. Our model predicted the material recognition performance of support vector machine (SVM) with 96% accuracy for the simulated data, with 92% accuracy for real-world data with constant initial sensor temperatures, and with 91% accuracy for real-world data with varied initial sensor temperatures. Using our model, we also provide insight into the roles of various factors on recognition performance, such as the temperature difference between the sensor and the object. Overall, our results suggest that our model could be used to help design better thermal sensors for robots and enable robots to use them more effectively.Comment: This article is currently under review for possible publicatio

Sensors for Robotic Hands: A Survey of State of the Art

Recent decades have seen significant progress in the field of artificial hands. Most of the surveys, which try to capture the latest developments in this field, focused on actuation and control systems of these devices. In this paper, our goal is to provide a comprehensive survey of the sensors for artificial hands. In order to present the evolution of the field, we cover five year periods starting at the turn of the millennium. At each period, we present the robot hands with a focus on their sensor systems dividing them into categories, such as prosthetics, research devices, and industrial end-effectors.We also cover the sensors developed for robot hand usage in each era. Finally, the period between 2010 and 2015 introduces the reader to the state of the art and also hints to the future directions in the sensor development for artificial hands

Challenges in flexible microsystem manufacturing : fabrication, robotic assembly, control, and packaging.

Microsystems have been investigated with renewed interest for the last three decades because of the emerging development of microelectromechanical system (MEMS) technology and the advancement of nanotechnology. The applications of microrobots and distributed sensors have the potential to revolutionize micro and nano manufacturing and have other important health applications for drug delivery and minimal invasive surgery. A class of microrobots studied in this thesis, such as the Solid Articulated Four Axis Microrobot (sAFAM) are driven by MEMS actuators, transmissions, and end-effectors realized by 3-Dimensional MEMS assembly. Another class of microrobots studied here, like those competing in the annual IEEE Mobile Microrobot Challenge event (MMC) are untethered and driven by external fields, such as magnetic fields generated by a focused permanent magnet. A third class of microsystems studied in this thesis includes distributed MEMS pressure sensors for robotic skin applications that are manufactured in the cleanroom and packaged in our lab. In this thesis, we discuss typical challenges associated with the fabrication, robotic assembly and packaging of these microsystems. For sAFAM we discuss challenges arising from pick and place manipulation under microscopic closed-loop control, as well as bonding and attachment of silicon MEMS microparts. For MMC, we discuss challenges arising from cooperative manipulation of microparts that advance the capabilities of magnetic micro-agents. Custom microrobotic hardware configured and demonstrated during this research (such as the NeXus microassembly station) include micro-positioners, microscopes, and controllers driven via LabVIEW. Finally, we also discuss challenges arising in distributed sensor manufacturing. We describe sensor fabrication steps using clean-room techniques on Kapton flexible substrates, and present results of lamination, interconnection and testing of such sensors are presented

Material recognition using tactile sensing

Identification of the material from which an object is made is of significant value for effective robotic grasping and manipulation. Characteristics of the material can be retrieved using different sensory modalities: vision based, tactile based or sound based. Compressibility, surface texture and thermal properties can each be retrieved from physical contact with an object using tactile sensors. This paper presents a method for collecting data using a biomimetic fingertip in contact with various materials and then using these data to classify the materials both individually and into groups of their type. Following acquisition of data, principal component analysis (PCA) is used to extract features. These features are used to train seven different classifiers and hybrid structures of these classifiers for comparison. For all materials, the artificial systems were evaluated against each other, compared with human performance and were all found to outperform human participants' average performance. These results highlighted the sensitive nature of the BioTAC sensors and pave the way for research that requires a sensitive and accurate approach such as vital signs monitoring using robotic systems