Robotic sensing is a subarea of robotics science intended to provide sensing capabilities to robots. Robotic sensing provides robots with the ability to sense their environments and is typically used as feedback to enable robots to adjust their behavior based on sensed input. Robot sensing includes the ability to see, touch, hear and move and associated algorithms to process and make use of environmental feedback and sensory data. Robot sensing is important in applications such as vehicular automation, robotic prosthetics, and for industrial, medical, entertainment and educational robots.
Vision
Method Visual sensing systems can be based on a variety of technologies and methods including the use of camera, sonar, laser and radio frequency identification (RFID) technology. All four methods aim for three procedures—sensation, estimation, and matching.
Image processing Image quality is important in applications that require excellent robotic vision. Algorithms based on wavelet transform that are used for fusing images of different spectra and different foci result in improved image quality. Robots can gather more accurate information from the resulting improved image.
Usage Visual sensors help robots to identify the surrounding environment and take appropriate action. Robots analyze the image of the immediate environment based on data input from the visual sensor. The result is compared to the ideal, intermediate or end image, so that appropriate movement or action can be determined to reach the intermediate or final goal.
Touch
Robot skin
Types and examples Examples of the current state of progress in the field of robot skins as of mid-2022 are a robotic finger covered in a type of manufactured living human skin, an electronic skin giving biological skin-like haptic sensations and touch/pain-sensitivity to a robotic hand, a system of an electronic skin and a human-machine interface that can enable remote sensed tactile perception, and wearable or robotic sensing of many hazardous substances and pathogens, and a multilayer tactile sensor hydrogel-based robot skin.
Tactile discrimination
Signal processing Touch sensory signals can be generated by the robot's own movements. It is important to identify only the external tactile signals for accurate operations. Previous solutions employed the Wiener filter, which relies on the prior knowledge of signal statistics that are assumed to be stationary. Recent solution applies an adaptive filter to the robot's logic. It enables the robot to predict the resulting sensor signals of its internal motions, screening these false signals out. The new method improves contact detection and reduces false interpretation.
Usage
Touch patterns enable robots to interpret human emotions in interactive applications. Four measurable features—force, contact time, repetition, and contact area change—can effectively categorize touch patterns through the temporal decision tree classifier to account for the time delay and associate them to human emotions with up to 83% accuracy. The Consistency Index is applied at the end to evaluate the level of confidence of the system to prevent inconsistent reactions. Robots use touch signals to map the profile of a surface in hostile environment such as a water pipe. Traditionally, a predetermined path was programmed into the robot. Currently, with the integration of touch sensors, the robots first acquire a random data point; the algorithm of the robot will then determine the ideal position of the next measurement according to a set of predefined geometric primitives. This improves the efficiency by 42%. In recent years, using touch as a stimulus for interaction has been the subject of much study. In 2010, the robot seal PARO was built, which reacts to many stimuli from human interaction, including touch. The therapeutic benefits of such human-robot interaction is still being studied, but has shown very positive results.
Hearing
Signal processing Accurate audio sensors require low internal noise contribution. Traditionally, audio sensors combine acoustical arrays and microphones to reduce internal noise level. Recent solutions combine also piezoelectric devices. These passive devices use the piezoelectric effect to transform force to voltage, so that the vibration that is causing the internal noise could be eliminated. On average, internal noise up to about 7dB can be reduced. Robots may interpret strayed noise as speech instructions. Current voice activity detection (VAD) system uses the complex spectrum circle centroid (CSCC) method and a maximum signal-to-noise ratio (SNR) beamformer. Because humans usually look at their partners when conducting conversations, the VAD system with two microphones enable the robot to locate the instructional speech by comparing the signal strengths of the two microphones. Current system is able to cope with background noise generated by televisions and sounding devices that come from the sides.
Usage Robots can perceive emotions through the way we talk and associated characteristics and features. Acoustic and linguistic features are generally used to characterize emotions. The combination of seven acoustic features and four linguistic features improves the recognition performance when compared to using only one set of features.
Acoustic feature Duration Energy Pitch Spectrum Cepstral Voice quality Wavelets
Linguistic feature Bag of words Part-of-speech Higher semantics Varia
Olfaction
Taste
For example, robot cooks may be able to taste food for dynamic cooking.
Motion perception
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