A document describes an algorithm created to estimate the mass placed on a sample verification sensor (SVS) designed for lunar or planetary robotic sample return missions. A novel SVS measures the capacitance between a rigid bottom plate and an elastic top membrane in seven locations. As additional sample material (soil and/or small rocks) is placed on the top membrane, the deformation of the membrane increases the capacitance. The mass estimation algorithm addresses both the calibration of each SVS channel, and also addresses how to combine the capacitances read from each of the seven channels into a single mass estimate. The probabilistic approach combines the channels according to the variance observed during the training phase, and provides not only the mass estimate, but also a value for the certainty of the estimate.
SVS capacitance data is collected for known masses under a wide variety of possible loading scenarios, though in all cases, the distribution of sample within the canister is expected to be approximately uniform. A capacitance-vs-mass curve is fitted to this data, and is subsequently used to determine the mass estimate for the single channel’s capacitance reading during the measurement phase. This results in seven different mass estimates, one for each SVS channel. Moreover, the variance of the calibration data is used to place a Gaussian probability distribution function (pdf) around this mass estimate. To blend these seven estimates, the seven pdfs are combined into a single Gaussian distribution function, providing the final mean and variance of the estimate. This blending technique essentially takes the final estimate as an average of the estimates of the seven channels, weighted by the inverse of the channel’s variance.
This work was done by Michael Wolf of Caltech for NASA’s Jet Propulsion Laboratory. NPO-48143
This Brief includes a Technical Support Package (TSP).
A Probabilistic Mass Estimation Algorithm for a Novel 7-Channel Capacitive Sample Verification Sensor
(reference NPO-48143) is currently available for download from the TSP library.
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Overview
The document presents a technical overview of a probabilistic mass estimation algorithm developed for a novel seven-channel capacitive sample verification sensor (SVS) used in NASA's sample return missions. The primary goal of this technology is to accurately estimate the mass of samples collected from extraterrestrial environments, such as the Moon or Mars, ensuring that the samples returned to Earth are properly quantified.
The SVS operates by measuring capacitance changes across its seven channels when a sample is placed on its membrane. Due to variations in sample loading and membrane deformation, a probabilistic approach is employed for calibration and mass estimation. The calibration phase involves placing known mass values on the sensor and recording the capacitance across all channels under various loading conditions. A capacitance-vs-mass curve is then fitted for each channel, typically using a second-order polynomial, to establish a relationship between capacitance readings and mass estimates.
During the estimation phase, when an unknown sample is placed on the sensor, the capacitance readings are observed, and individual mass estimates are generated for each channel based on the previously established curves. Each estimate is treated as a mean of a probability distribution, with variances derived from the calibration data. The algorithm accounts for the uncertainty in capacitance readings and interpolates variances along the capacitance-mass curve to refine the mass estimates.
The final mass estimate is obtained by combining the individual probability distributions from all seven channels into a single Gaussian distribution. This is achieved using Bayes’ Rule, which allows for the integration of the individual channel estimates while considering their variances. The mean of this combined distribution serves as the final mass estimate, while the uncertainty is quantified by the variance of the combined distribution.
The document emphasizes the importance of this technology in enhancing the accuracy of mass measurements in sample return missions, which is critical for scientific analysis and understanding of extraterrestrial materials. The research was conducted at NASA's Jet Propulsion Laboratory (JPL) and highlights the innovative approaches being developed to support future space exploration endeavors.
