Search NASA⌕ Search

NASA NTRS · 20130001780

Humanlike Robots - Synthetically Mimicking Humans

Abstract

Nature inspired many inventions and the field of technology that is based on the mimicking or inspiration of nature is widely known as Biomimetics and it is increasingly leading to many new capabilities. There are numerous examples of biomimetic successes including the copying of fins for swimming, and the inspiration of the insects and birds flight. More and more commercial implementations of biomimetics are appearing and behaving lifelike and applications are emerging that are important to our daily life. Making humanlike robots is the ultimate challenge to biomimetics and, for many years, it was considered science fiction, but such robots are becoming an engineering reality. Advances in producing such robot are allowing them to perform impressive functions and tasks. The development of such robots involves addressing many challenges and is raising concerns that are related to fear of their application implications and potential ethical issues. In this paper, the state-of-the-art of humanlike robots, potential applications and challenges will be reviewed.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bar-Cohen, Yoseph. 2012-06-11. Humanlike Robots - Synthetically Mimicking Humans. https://ntrs.nasa.gov/citations/20130001780

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Analytical Redundancy Using Kalman Filters for Rocket Engine Sensor Validation

The use of sensor redundancy is crucial in aerospace systems to maintain safe, reliable operation. While hardware redundancy is more common in application, analytical redundancy can provide a viable alternative in systems where the installation of multiple redundant sensors is not viable. To this end, the use of Kalman filters to analytically validate sensor measurements within rocket engines was explored. First, a dynamic model of the RS 25 engine, a derivative of the Space Shuttle Main Engine (SSME), was reduced to a subset of relations, focused around the main combustion chamber pressure. These relations were used within the Kalman filter algorithm to generate an estimate of sensor measurements to be compared with true measurements for data validation purposes. By using a bank of Kalman filters, the residuals between the estimated and true measurements were used to detect and isolate sensor faults. Through fault simulations, the sensor validation performance of this Kalman filter bank design was compared to a hardware redundancy check. Sensor bias and drift faults of various magnitudes were injected into nominal RS 25 engine test data. Results for both approaches show comparable fault detection with most bias faults found nearly instantaneously by both algorithms. Drift fault detection results show certain cases where one algorithm is faster than the other. The key advantage of the Kalman filter algorithm is shown in fault isolation performance where it can isolate faults between two redundant sensors while the hardware redundancy comparisons cannot.

sensors↗

Suomi NPP VIIRS Solar Diffuser Screen Transmittance Model and Its Applications

The visible infrared imaging radiometer suite on the Suomi National Polar-orbiting Partnership satellite calibrates its reflective solar bands through observations of a sunlit solar diffuser (SD) panel. Sunlight passes through a perforated plate, referred to as the SD screen, before reaching the SD. It is critical to know whether the SD screen transmittance measured prelaunch is accurate. Several factors such as misalignments of the SD panel and the measurement apparatus could lead to errors in the measured transmittance and thus adversely impact on-orbit calibration quality through the SD. We develop a mathematical model to describe the transmittance as a function of the angles that incident light makes with the SD screen, and apply the model to fit the prelaunch measured transmittance. The results reveal that the model does not reproduce the measured transmittance unless the size of the apertures in the SD screen is quite different from the design value. We attribute the difference to the orientation alignment errors for the SD panel and the measurement apparatus. We model the alignment errors and apply our transmittance model to fit the prelaunch transmittance to retrieve the "true" transmittance. To use this model correctly, we also examine the finite source size effect on the transmittance. Furthermore, we compare the product of the retrieved "true" transmittance and the prelaunch SD bidirectional reflectance distribution function (BRDF) value to the value derived from on-orbit data to determine whether the prelaunch SD BRDF value is relatively accurate. The model is significant in that it can evaluate whether the SD screen transmittance measured prelaunch is accurate and help retrieve the true transmittance from the transmittance with measurement errors, consequently resulting in a more accurate sensor data product by the same amount.

sensors↗

Fiber Optic Temperature Sensors in TPS: Arc Jet Model Design & Testing

Techniques for using fiber optics with Fiber Bragg Gratings (FBGs) have been developed by IFOS Corp. for use in thermal protection systems (TPS) on spacecraft heat shield materials through NASA Phase 1 and 2 SBIR efforts and have been further improved in a recent collaboration between IFOS and NASA that will be described here. Fiber optic temperature sensors offer several potential advantages over traditional thermocouple sensors including a) multiplexing many sensors in a single fiber to increase sensor density in a given array or to provide spatial resolution, b) improved thermal property match between sensor and TPS to reduce heat flow disruption, c) lack of electrical conductivity.

sensors↗