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

Augmenting variable stiffness actuation using reaction wheels

A branch of robotics, variable impedance actuation, along with one of its subfields variable stiffness actuation (VSA), is gaining momentum recently. There have been many thorough studies earlier in the design and recently in the control of these systems. The performance of these systems is mainly limited by their physical constraints, such as actuator nominal torque and maximum elastic element stiffness. This paper discusses the integration of reaction wheels to VSA systems and using reactive torques to improve the performance of the combined system. Since the compliant nature of VSA mechanisms is often associated with cyclic motion, reactive torques can be used to amplify the robot motion and accumulate more energy in the elastic elements in a given period of time. After presenting our modeling and control framework for reaction wheel-integrated VSA robots, we benchmark the performance of a reaction wheel-integrated VSA system using an explosive ball throwing task. Specifically, extensive simulation and real-world experiments are conducted with three different configurations: VSA-only, reaction wheel-only, and reaction wheel-integrated VSA. The results of these experiments show the benefits of reaction wheel-integrated VSA robots compared with the two other configurations

End-to-End Deep Diagnosis of X-ray Images

In this work, we present an end-to-end deep learning framework for X-ray image diagnosis. As the first step, our system determines whether a submitted image is an X-ray or not. After it classifies the type of the X-ray, it runs the dedicated abnormality classification network. In this work, we only focus on the chest X-rays for abnormality classification. However, the system can be extended to other X-ray types easily. Our deep learning classifiers are based on DenseNet-121 architecture. The test set accuracy obtained for 'X-ray or Not', 'X-ray Type Classification', and 'Chest Abnormality Classification' tasks are 0.987, 0.976, and 0.947, respectively, resulting into an end-to-end accuracy of 0.91. For achieving better results than the state-of-the-art in the 'Chest Abnormality Classification', we utilize the new RAdam optimizer. We also use Gradient-weighted Class Activation Mapping for visual explanation of the results. Our results show the feasibility of a generalized online projectional radiography diagnosis system.Comment: 4 pages, 5 figure

Color-Coded Fiber-Optic Tactile Sensor for an Elastomeric Robot Skin

The sense of touch is essential for reliable mapping between the environment and a robot which interacts physically with objects. Presumably, an artificial tactile skin would facilitate safe interaction of the robots with the environment. In this work, we present our color-coded tactile sensor, incorporating plastic optical fibers (POF), transparent silicone rubber and an off-the-shelf color camera. Processing electronics are placed away from the sensing surface to make the sensor robust to harsh environments. Contact localization is possible thanks to the lower number of light sources compared to the number of camera POFs. Classical machine learning techniques and a hierarchical classification scheme were used for contact localization. Specifically, we generated the mapping from stimulation to sensation of a robotic perception system using our sensor. We achieved a force sensing range up to 18 N with the force resolution of around 3.6~N and the spatial resolution of 8~mm. The color-coded tactile sensor is suitable for tactile exploration and might enable further innovations in robust tactile sensing.Comment: Presented at ICRA2019, Montrea

SpeakingFaces: A Large-Scale Multimodal Dataset of Voice Commands with Visual and Thermal Video Streams

We present SpeakingFaces as a publicly-available large-scale dataset developed to support multimodal machine learning research in contexts that utilize a combination of thermal, visual, and audio data streams; examples include human-computer interaction (HCI), biometric authentication, recognition systems, domain transfer, and speech recognition. SpeakingFaces is comprised of well-aligned high-resolution thermal and visual spectra image streams of fully-framed faces synchronized with audio recordings of each subject speaking approximately 100 imperative phrases. Data were collected from 142 subjects, yielding over 13,000 instances of synchronized data (~3.8 TB). For technical validation, we demonstrate two baseline examples. The first baseline shows classification by gender, utilizing different combinations of the three data streams in both clean and noisy environments. The second example consists of thermal-to-visual facial image translation, as an instance of domain transfer.Comment: 6 pages, 4 figures, 3 table

Generalized Dynamics of Stacked Tensegrity Manipulators

https://ieeexplore.ieee.org/document/8713969Tensegrity structures emerged initially as an art form, have recently gained substantial interest among engineering researchers. The distinctive attribute of these structures is using pretensioned tensile elements connected to rigid bars to establish an equilibrium of the whole structure. Thanks to these elements, tensegrity structures are lightweight and yet robust. The main challenge impeding their widespread use is the intricate constrained nonlinear dynamics caused by the tensegrity topology. In this paper, we extend the dynamics of tensegrities by adding damping forces and incorporating forces along the connected strings passing through several nodes. As an experimental platform, a two-stage stacked tensegrity manipulator was constructed. The system was actuated using six actuators and the kinematic information of the system was acquired by measuring the node coordinates using optical motion capture. Afterward, we compared the structure behavior to the simulated one using our dynamics formulation. The results of these experiments show that our dynamics formulation is capable of representing the rich nonlinear dynamics of stacked tensegrity manipulators effectively