Robotic sensing

From Wikipedia, the free encyclopedia
Jump to: navigation, search


Robotic sensing is a subarea of robotics science intended to give robots sensing capabilities, so that robots are more human-like. Robotic sensing mainly gives robots the ability to see,[1][2][3] touch,[4][5][6] hear[7] and move[8][9][10] and uses algorithms that require environmental feedback.

Vision[edit]

Method[edit]

The visual sensing system can be based on anything from the traditional camera, sonar, and laser to the new technology radio frequency identification (RFID),[1] which transmits radio signals to a tag on an object that emits back an identification code. All four methods aim for three procedures—sensation, estimation, and matching.

Image processing[edit]

Image quality is important in applications that require excellent robotic vision. Algorithm based on wavelet transform for fusing images of different spectra and different foci improves image quality.[2] Robots can gather more accurate information from the resulting improved image.

Usage[edit]

Visual sensors help robots to identify the surrounding and take appropriate action.[3] Robots analyze the image of the immediate environment imported from the visual sensor. The result is compared to the ideal intermediate or end image, so that appropriate movement can be determined to reach the intermediate or final goal.

Touch[edit]

[11]

Signal processing[edit]

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.[4] 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[edit]

[12] 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.[5] The Consistency Index[5] 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[6] 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%.[5]

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.[13]

Hearing[edit]

Signal processing[edit]

Accurate audio sensor requires low internal noise contribution. Traditionally, audio sensors combine acoustical arrays and microphones to reduce internal noise level. Recent solutions combine also piezoelectric devices.[7] 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.[7]

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.[14] 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[edit]

Robots can perceive our emotion through the way we talk. 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.[15]

Acoustic feature[edit]

Linguistic feature[edit]

Movement[edit]

Usage[edit]

Automated robots require a guidance system to determine the ideal path to perform its task. However, in the molecular scale, nano-robots lack such guidance system because individual molecules cannot store complex motions and programs. Therefore, the only way to achieve motion in such environment is to replace sensors with chemical reactions. Currently, a molecular spider that has one streptavidin molecule as an inert body and three catalytic legs is able to start, follow, turn and stop when came across different DNA origami.[8] The DNA-based nano-robots can move over 100 nm with a speed of 3 nm/min.[8]

In a TSI operation, which is an effective way to identify tumors and potentially cancer by measuring the distributed pressure at the sensor’s contacting surface, excessive force may inflict a damage and have the chance of destroying the tissue. The application of robotic control to determine the ideal path of operation can reduce the maximum forces by 35% and gain a 50% increase in accuracy[9] compared to human doctors.

Performance[edit]

Efficient robotic exploration saves time and resources. The efficiency is measured by optimality and competitiveness. Optimal boundary exploration is possible only when a robot has square sensing area, starts at the boundary, and uses the Manhattan metric.[10] In complicated geometries and settings, a square sensing area is more efficient and can achieve better competitiveness regardless of the metric and of the starting point.[10]

See also[edit]

References[edit]

  1. ^ a b Roh SG, Choi HR (Jan 2009). "3-D Tag-Based RFID System for Recognition of Object." IEEE Transactions on Automation Science and Engineering 6 (1): 55–65.
  2. ^ a b Arivazhagan S, Ganesan L, Kumar TGS (Jun 2009). "A modified statistical approach for image fusion using wavelet transform." Signal Image and Video Processing 3 (2): 137-144.
  3. ^ a b Jafar FA, et al (Mar 2011). "An Environmental Visual Features Based Navigation Method for Autonomous Mobile Robots." International Journal of Innovative Computing, Information and Control 7 (3): 1341-1355.
  4. ^ a b Anderson S, et al (Dec 2010). "Adaptive Cancelation of Self-Generated Sensory Signals in a Whisking Robot." IEEE Transactions on Robotics 26 (6): 1065-1076.
  5. ^ a b c d Kim YM, et al (Aug 2010)."A Robust Online Touch Pattern Recognition for Dynamic Human-robot Interaction." IEEE Transactions on Consumer Electronics 56 (3): 1979-1987.
  6. ^ a b Mazzini F, et al (Feb 2011). "Tactile Robotic Mapping of Unknown Surfaces, with Application to Oil Wells." IEEE Transactions on Instrumentation and Measurement 60 (2): 420-429.
  7. ^ a b c Matsumoto M, Hashimoto S (2010). "Internal Noise Reduction Using Piezoelectric Device under Blind Condition." Internatl (Jan 2011). "Searching for the most important feature types signalling emotion-related user states in speech." Computer Speech and Language 25 (1): 4-28.
  8. ^ a b c Lund K, et al (May 2010). "Molecular robots guided by prescriptive landscapes." Nature 465 (7295): 206-210.
  9. ^ a b Trejos AL, et al (Sep 2009). "Robot-assisted Tactile Sensing for Minimally Invasive Tumor Localization." International Journal of Robotics Research 28 (9): 1118-1133.
  10. ^ a b c Czyzowicz J, Labourel A, Pelc A (Jan 2011). "Optimality and Competitiveness of Exploring Polygons by Mobile Robots." Information and Computation 209 (1): 74-88.
  11. ^ [1]
  12. ^ [2]
  13. ^ https://www.youtube.com/watch?v=oJq5PQZHU-I
  14. ^ Kim HD, et al (2009). "Target Speech Detection and Separation for Communication with Humanoid Robots in Noisy Home Environments." Advanced Robotics 23 (15): 2093-2111.
  15. ^ Batliner A, et al (Jan 2011). "Searching for the most important feature types signalling emotion-related user states in speech." Computer Speech and Language 25 (1): 4-28.