Sensor

Sensor

A sensor is a device that detects and responds to some type of input from the physical environment. The specific input could be light, heat, motion, moisture, pressure, or any one of a great number of other environmental phenomena. The output is generally a signal that is converted to human-readable display at the sensor location or transmitted electronically over a network for reading or further processing. 

Here are a few examples of the many different types of sensors:

In a mercury-based glass thermometer, the input is temperature. The liquid contained expands and contracts in response, causing the level to be higher or lower on the marked gauge, which is human-readable. 

An oxygen sensor in a car's emission control system detects the gasoline/oxygen ratio, usually through a chemical reaction that generates a voltage. A computer in the engine reads the voltage and, if the mixture is not optimal, readjusts the balance. 

Temperature is the most-measured process variable in industrial automation. Most commonly, a temperature sensor is used to convert temperature value to an electrical value. Temperature Sensors are the key to read temperatures correctly and to control temperature in industrials applications.

A large distinction can be made between temperature sensor types. Sensors differ a lot in properties such as contact-way, temperature range, calibrating method and sensing element. The temperature sensors contain a sensing element enclosed in housings of plastic or metal. With the help of conditioning circuits, the sensor will reflect the change of environmental temperature.

In the temperature functional module we developed, we use the LM34 series of temperature sensors. The LM34 series are precision integrated-circuit temperature sensors, whose output voltage is linearly proportional to the Fahrenheit temperature. The LM34 thus has an advantage over linear temperature sensors calibrated in degrees Kelvin, as the user is not required to subtract a large constant voltage from its output to obtain convenient Fahrenheit scaling. The LM34 does not require any external calibration or trimming to provide typical accuracies of ±1.2°F at room temperature and ±11.2°F over a full -50 to +300°F temperature range. The LM34 is rated to operate over a -50° to +300°F temperature range.

It is easy to include the LM34 series in a temperature measuring application. The output voltage of LM34 is linearly proportional to the Fahrenheit temperature, it has a Linear +10.0 mV/°F scale factor which means that you will get  n*10.0 mV output voltage if the environment  temperature is n°F.

The LM34 series is available packaged in hermetic TO-46 transistor packages, while the LM34C, LM34CA and LM34D are also available in the plastic TO-92 transistor package. The LM34D is also available in an 8-lead surface mount small outline package. In our functional module, LM34H in metal can package (TO-46) is used in the functional module, it is very important to know that the wiring of sensor should be based on the positions of the leading pins in different packages.

DESCRIPTION OF TEMPERATURE SENSOR FUNCTIONAL MODULE

The temperature sensor functional module consists of two parts: the function module box and the probe head. The LM34 temperature sensor is mounted on the probe head. Be careful to make sure that the sensor is properly mounted on the probe head. (refer to Figure 4 Labeled picture of the probe head.)

Labeled picture of the temperature sensor circuit functional module.

By replacing the LM34 with another precision integrated-circuit temperature sensor LM35, we can easily get an output voltage proportional to the centigrade temperature. The LM35 sensor has a linear +10.0 mV/°C scale factor and a temperature range from

-55°C to +150°C.In fact LM34 and LM35 are among the same series of temperature sensors so that they can be easily exchanged in different applications. The wiring for

LM 35 is the same as that of LM34. Please refer to the datasheets of LM34 and LM35 for more detailed packaging and features information.

Optical Sensors

 

Introduction

The two major oceanographic applications of optics, aside from laser communications systems, are radiometric measurements in the visible wavebands and individual particle imaging and analysis.  Radiometric measurements serve as proxies for important biogeochemical variables in the ocean primarily dissolved organic materials, marine phytoplankton, and other particles including bioluminescent organisms, biogenic calcium carbonate and suspended sediments.  The optical sensors used for these applications include passive radiometers and active systems with integrated light sources.  The incorporation of radiometric sensors into A/L (autonomous and Lagrangian) platforms enables simultaneous measurement of biogeochemical entities on the same space and time scale as physical measurements.  Depending on the platform trajectory, radiometric optical sensors offer the potential for resolving responses of the carbon cycle to climatic changes in mixed layer processes; for detecting harmful algal blooms; for providing a vertical dimension to surface phytoplankton and other particle fields derived from satellite ocean color imagery; and for providing data on phytoplankton concentrations in regions of persistent cloud cover, such as the North Atlantic Ocean during the spring bloom.  Radiometric optical sensing is maturing rapidly and a number of optical sensors have already been incorporated into A/L platforms.

Particle imaging systems include still, holographic, and video cameras for imaging organisms that range in size from small phytoplankton to large zooplankton and small fish.  One of the primary development goals of this field has been to obtain images of plankton that are sufficient to resolve their taxonomic identity; as a consequence, research in this area has focused on pattern recognition algorithms as well as optics. As an alternative to traditional sampling methods, imaging approaches are particularly useful in studying fragile organisms  (e.g., gelatinous plankton) and particles (e.g., marine snow).  The powerful union of imaging with other sampling methodologies is leading to breakthroughs in rapid identification of planktonic organisms; for example the combination of imaging systems with acoustics methods of biomass assessment for zooplankton and the combination of microscopic imaging with molecular probes for phytoplankton species identification.  Flow cytometry is primarily a non-imaging particle analysis technology that provides detailed scattering and fluorescence data on individual particles; its application in oceanography has primarily focused on phytoplankton. Imaging and flow cytometric systems have been used on or deployed from ships for a number of years, and more recently have been deployed on moorings and incorporated into powered AUVs.  The incorporation of these systems into A/L platforms can offer a new approach to studying how plankton community structure (i.e., total species composition) responds to interannual or climatic variability, species invasions or removal, harmful algal blooms, etc.   For example, unattended and persistent imaged sampling could make a significant contribution toward resolving the poorly understood role of gelatinous zooplankton in ocean ecosystems; little is known about gelatinous plankton blooms largely because they are patchy, ephemeral, and under sampled. 

There is a major distinction between the type and size of optical sensing systems that can presently be incorporated into powered AUVs and the more stringent size and power constraints for sensors that can be incorporated into drifters, profilers, and gliders.  This white paper will focus primarily on the radiometric sensors because their technology is presently more compatible with the smaller package size and lower power consumption required for integration into floats and gliders.

Background for Radiometric Sensing

Optically Detectable Biogeochemical Variables: 

Phytoplankton, other particles, and chromophoric dissolved organic matter (CDOM) are amenable to radiometric optical sensing because they absorb, scatter, attenuate, and fluoresce light with characteristic optical patterns.   Phytoplankton, as photosynthetic organisms, absorb electromagnetic radiation predominately in the blue, blue-green, and red portions of the visible spectrum with the specific spectra determined by the pigment composition.  As particles, phytoplankton scatter light; the shape of the scattering spectrum is dependent on phytoplankton size (with a world-wide size range from approximately 0.7 mm to about 100 mm), composition, and absorption spectrum.  Other non-algal organic particles, such as bacteria and detritus, are relatively weak absorbers with their absorption maxima in the UV.  Similar to phytoplankton, the shape of their scattering spectrum is dependent upon their size distribution. Suspended sediments are predominately scatterers, rather than absorbers, although strong absorption features have been observed in mineralic sediments, particularly iron-rich minerals (Babin et al. 2002). The spectral slope of their scattering coefficients is dominated by size and varies from a spectral slope on the order of l^-1 for the smallest marine particles to l⁰ for larger particles.  The ratio of backscattering to total scattering for mineral particles is three-to-six times greater than for phytoplankton (Twordowski et al., 2001).  CDOM absorbs most strongly in the UV with absorption peaks associated with humic and fulvic acids, MAAs, nucleic acids, and specific amino acids.  The overall exponential decrease in absorption from the UV to the visible has a mean exponential slope of –0.018 (Roesler et al. 1989).

Proxies:     

The absorption [a], scattering [b], and attenuation [c = a + b] coefficients can be used as proxies for biogeochemical variables.  To first order, the magnitude of a, b, and c are proportional to the concentration of the optically active constituents. The magnitude of the phytoplankton absorption coefficient, for example, is approximately proportional to phytoplankton biomass.  However, several caveats do apply.  For example phytoplankton living at low irradiances at depth will have slightly increased pigment absorption per unit biomass as a compensation for the low light environment. In addition, the absorption efficiency per unit biomass of phytoplankton will vary spectrally as a function of species composition.  

The coefficients of individual optically-active constituents of seawater are additive; hence,

a-total  =   a-phytoplankton + a-other particles + a-CDOM  +  a-seawater.

Because each constituent may contribute to a coefficient at any single wavelength (i.e., CDOM, other particles, and phytoplankton all absorb at 400 nm), it may be necessary to use a combination of measurements to adequately resolve the biogeochemical variable of interest.

IOPs and AOPs:   

Absorption, scattering, and attenuation coefficients are IOPs (inherent optical properties, i.e., properties not affected by the natural illumination conditions). As a consequence of absorption and scattering in the water column, the magnitude and spectrum of underwater light field is modified.  In other words, the IOPs modify the incoming solar radiation, thereby determining the characteristics of the underwater light field.  The characteristics of the light field are known as apparent optical properties (AOPs) and include Ed (downwelling irradiance), Lu (upwelling radiance), Kd (diffuse attenuation coefficent), and reflectance.   IOPs and AOPs are related via the radiative transfer equation, and IOPs can be derived from AOPs through solution or inversion of the radiative transfer equation (cf. Gordon et al., 1975).

AOPs can themselves serve as direct proxies for biogeochemical variables. For example, solar-stimulated chlorophyll fluorescence, derived from an increase in Lu (upwelling radiance) at 683 nm, is a proxy for phytoplankton concentration.  The diffuse attenuation coefficient (Kd), derived from Ed (downwelling irradiance), can serve as a proxy for either for phytoplankton concentration in waters lacking inorganic particles or for total particle concentration.  In addition to serving as proxies, AOPs provide additional information for understanding ocean biogeochemical processes.  Irradiance measurements as a function of depth are required inputs for water-column primary production models.  Simultaneous measurements of irradiance enable better parameterizations of the light- and time-dependent quenching of chlorophyll fluorescence and photo-oxidation of CDOM.  Reflectance is the ratio of upwelling radiance to downwelling irradiance; improved near-surface reflectance measurements provide valuable information that can be used to improve interpretation of ocean color satellite remote sensing data.

Fluorescence:  

Both phytoplankton and CDOM have the ability to fluorescence, or re-emit light within distinct wavebands that are slightly longer than the absorbed light.  The magnitude of fluorescence emitted is proportional to the concentration of the fluorescing material under specified conditions (i.e., constant excitation irradiance, molar absorption coefficient, and fluorescence quantum yield). Chlorophyll a is the primary fluorescent pigment in phytoplankton, with a maximum fluorescence emission at 683 nm.  Phycoerthyrin is an orange-fluorescing pigment found only in two groups of phytoplankton (cyanobacteria and cryptomonads), and hence can be used as a diagnostic of the presence and abundance of those taxonomic groups.  CDOM fluorescence can be excited at most wavelengths in the UV and blue, with emission spanning blue to green; for example, excitation stimulated at 370 nm can be measured around 450 nm.  The use of fluorescence as a proxy can be more challenging than the use of absorption, scattering, and attenuation coefficients, because of the potential for variability in the fluorescence quantum yield.  For example, phytoplankton fluorescence varies due to nutrient limitation and to photoquenching, which is dependent on the daily illumination history.

Bioluminescence: 

Marine bioluminescence also can be considered as a special optical case, rather than as IOP or an AOP.  Bioluminescence is the result of an exothermic internal chemical reaction that releases energy as light, either as an apparently spontaneous event or in response to mechanical stimulation.  A subset of species of bacteria, dinoflagellates, zooplankton, and fish are bioluminescent. Bioluminescence is measured with very sensitive passive radiometers able to detect the very low light levels associated with night-time bioluminescence.  Because bioluminescence is not a feature of all organisms, the magnitude of bioluminescence cannot be used to quantify total zooplankton biomass.  However, the measurement of bioluminescence is a valuable diagnostic of the presence and abundance of specific species.  In addition, the prediction of bioluminescence has important considerations in naval operations.

Present Status of Radiometric Optical Sensing

Passive vs. Active Sensors:     

Optical sensors include passive and active radiometric systems.  The nomenclature is based on whether the light source is independent of the sensor (i.e., the sun for a passive sensor) or is an integral component of the system (i.e., a LED or lamp for an active sensor).  Passive sensors are used to measure AOPs (Ed, Lu, solar-stimulated fluorescence, Kd, reflectance) and bioluminescence.  Because AOP instruments are passive sensors and depend only on sunlight, they consume less power than the IOP sensors.  However, they cannot operate at night and they are restricted to measurement within the photic zone (i.e., 10 to 150 m, depending on the concentration of optically active material in the water).  Active sensors are used to directly measure IOPs (absorption, scattering, and attenuation coefficients) and fluorescence.  Because they have an internal light source, the IOP sensors generally have higher power consumption rates than passive sensors.  In contrast to passive sensors, they can operate at night and below the photic zone, thereby making it possible to detect phenomena such as diel changes in particle concentration, sinking phytoplankton aggregations, and detached nephaloid layers. In addition active sensors can provide optical properties at wavelengths that have reduced penetration below the oceans surface, such as UV and infrared wavelengths.

Recent A/L Deployments:    

The incorporation of radiometer optical sensors into powered AUVs has been highly successful.  Over the last few years AUVs carrying multi-wavelength spectrometers, bioluminescence detectors, hyperspectral upwelling and downwelling radiometers, etc. have become integral components of science missions along the east coast of the U.S., off the Florida shelf, and off California.  In the last few years a number of successful float and glider missions incorporating small radiometric sensors have been carried out.  A few examples are chlorophyll fluorometers on surface drifters (Abbott and Letelier, 1998) beam c attenuation meters on profiling drifters (Bishop et al., 2002), downwelling irradiance sensors with on-board computation of Kd on profiling floats (Mitchell et al., 2000), and backscattering and chlorophyll fluorescence on gliders (Perry et al., 2003).

Challenges

 Some of the challenges for developing optical sensors for A/L platforms are generic, while others are more or less stringent depending on the platform constraints and intended mission duration.  For example and in contrast to floats and gliders, powered AUVs can accommodate larger payloads and sensors with higher power demands, such as multi-wavelength spectrometers, imaging systems, and flow cytometers.  Only radiometric sensors that are sufficiently small and power conservative have been incorporated into floats and gliders (i.e., downwelling irradiance, beam c, backscattering, and fluorescence sensors).  Other engineering challenges become important only for longer deployments - for missions of short duration (i.e., days) issues of biofouling and sensor drift are not as great a concern as they are for missions lasting from months to years.

Through partnerships within the ocean community and in concert with the engineering and other communities, it is hoped that solutions can be developed to address the issues that limit the full potential of optical sensors and systems.  Issues include:

*   Sensor system size

*   Power demand

*   Dynamic range (from eutrophic bay to the open ocean; from the surface to the ocean bottom)

*   Sensor calibration, quality control and quality assurance

*   Stability (long term drift and material longevity)

*   Reliability and failure

*   Biofouling of optical surfaces

*   Water volume sampled and optimized sampling rate

*   Issues with interpretation and conversion of optical proxies to the biogeochemical entities (e.g., IOP vs. AOP sensors; the daytime fluorescence quenching problem; image recognition)

*   Data volume and robust algorithms for on-board data analysis

*   Geostatistical methodology for spatial – temporal analysis of L/A data

     Tactile sensing has been a component of robotics for roughly as long as vision. However, in comparison to vision, for which great strides have been made in terms of hardware and software and which is now widely used in industrial and mobile robot applications, tactile sensing always seems to be “a few years away” from widespread utility. Therefore, before reviewing the technologies and approaches available it is worthwhile to consider some basic questions:

• How important is tactile sensing?
• What is it useful for?
• Why does it remain comparatively undeveloped?

 

In Nature, tactile sensing is evidently an essential survival tool. Even the simplest creatures are endowed with large numbers of mechanoreceptors for exploring and responding to various stimuli. In humans, tactile sensing is indispensable for manipulation, exploration and response. A couple of quick thought exercises illustrate the point: When our fingers are numbed by cold we become clumsy, so that simple manipulation tasks, like unbuckling a boot, are an exercise in frustration. Our muscles, snug in our coat sleeves, are only slightly affected but our cutaneous mechanoreceptors are anesthetized. For exploration, we rapidly assimilate tactile information about material and surface properties (e.g., hardness, thermal conductivity, friction, roughness) to help us identify objects. We may have difficulty distinguishing leather from pleather™ by sight, but not by touch! The importance of tactile response, whether to a gentle touch or an impact, is seen in the damage that patients with peripheral neuropathy (e.g., as a complication of diabetes) accidentally do to themselves.

As table I indicates, the same functional categories apply to robots. However, in comparison to animals, with thousands to millions of mechanoreceptors per square centimeter of skin , even the most sophisticated robots are impoverished. One reason for the slow development of tactile sensing technology as compared to vision is that there is no tactile analog to the CCD or COMS optical array. Instead, tactile sensors elicit information through physical interaction. They must be incorporated into gripping or “skin” surfaces with compliance, for conforming locally to surfaces, and adequate friction for handling objects securely. The sensors and skin must also be robust enough to survive repeated impacts and abrasions. And unlike an image plane, tactile sensors must be distributed over the robot appendages, with particularly high concentrations in areas such as the fingertips. The wiring of tactile sensors is consequently another challenge.

A second set of difficulties arises from the inherently multi-modal nature of tactile sensing. In humans, there are four main types of mechanoreceptors which can be classified according to whether they are slow- or fast-adapting and whether they have large or small receptive fields . For example, when you hold your fingertips against the edge of the table you can feel the corner as a continuing effect; the receptors that are primarily responsible for the sensation are slow-adapting Meissner and Merkel corpuscles, which detect local pressure and skin-stretch. In contrast, the detection of surface scratches in the tabletop requires motion of the fingertips across the surface, which excites the fast-adapting Pacinian corpuscles. For robots to make full use tactile information a similarly multi-modal approach, often employing different transducers, is required.

Despite the challenges associated with tactile sensing, interactive and multi-modal as it is, considerable progress in sensor design and deployment has been made over the last couple of decades. In the following sections we review the main functional classes of tactile sensors and discuss their relative strengths and limitations. Looking ahead, new fabrication techniques offer the possibility of artificial skin materials with integrated sensors and local processing for interpreting sensor signals and communicating over a common buss to reduce wiring. 

Tactile sensor types

Single sensors – are most commonly force/torque sensors, dynamic sensors and thermal sensors.

 Force/torque sensors

are often used in combination with tactile arrays to provide information for force control. A single force/torque sensor can sense loads anywhere on the distal link of a manipulator and, not being subject to the same packaging constraints as a “skin” sensor, can generally provide more precise force measurements at higher bandwidth. If the geometry of the manipulator link is defined, and if single-point contact can be assumed (as in the case of a robot finger with a hemispherical tip contacting locally convex surfaces), then a force/torque sensor can provide information about the contact location by ratios of forces and moments in a technique called “intrinsic tactile sensing” [Salisbury, Bicchi].

Dynamic tactile sensors

The most common dynamic tactile sensors are small accelerometers at the fingertips or in the skin of a robotic finger. They function roughly like pacinian corpuscles in humans [cite] and have a correspondingly large receptive field so that one or two skin accelerometers suffices for an entire finger. These sensors are particularly effective for detecting the making and breaking of contact, the onset of slip and the vibrations associated with sliding over textured surfaces.

A second type of dynamic tactile sensor is the stress rate sensor [cite Howe, Son]. If a fingertip is sliding at a speed of a few centimeters/second over small asperities (bumps or pits) in a surface, the transient changes in stresses in the skin will be significant. A piezoelectric polymer such as PVDF [cite] that produces a charge in response to strain can be used to produce currents proportional to the rage of change of stress:

[could put a small diagram here of dynamic tactile sensors + stress rate circuit]

Thermal sensors

Thermal sensors are an important component of the human ability to identify the materials of which objects are made (think of how metal feels cool to the touch compared to wood) but little used in robotics. Human thermal sensing involves detecting thermal gradients in the skin, which correspond to both the temperature and the thermal conductivity of an object. Robotic  thermal sensors have involved peltier junctions in combination with thermocouples or thermistors [cite].

Difficulties have been encountered in obtaining sufficient resolution and time response when using them to distinguish among different materials [cite ]

See a bit more at

Sensor arrays

- there are various possible ways of organizing tactile sensor arrays. From a functional standpoint, the primary concerns include:

• What is being measured (e.g., surface pressure or shear tractions, deformations, local geometry)
• What is the transduction method (e.g., piezo resistive, capacitive, optical)
• What are the mounting provisions (e.g., rigid or compliant, flat or curved)
• What are the expected levels of sensor resolution, accuracy and dynamic range (e.g. point to point spacing, minimum detectable stimulus, hysteresis, frequency response).