Some mapping properties of rc and rl driving- point impedance functions.
Mapping properties of RC and RL driving point impedance functions in right half of frequency plane
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RC-RL decomposition of a fourth-order polynomial
Fourth order polynomial RC-RL decomposition conditions
Chan, S. P.↗
RL-34 ring laser gyro laboratory evaluation for the Deep Space Network antenna application
The overall results of this laboratory evaluation are quite encouraging. The gyro data is in good agreement with the system's overall pointing performance, which is quite close to the technical objectives for the Deep Space Network (DSN) application. The system can be calibrated to the levels required for millidegree levels of pointing performance, and initialization performance is within the required 0.001 degree objective. The blind target acquisition performance is within a factor of two of the 0.0001 degree objective, limited only by a combination of the slow rate (0.5 deg/sec) and the existing production quantization logic (0.38 arc-sec/pulse). Logic circuitry exists to better this performance such that it will better the objective by 50 percent. Representative data with this circuitry has been provided for illustration. Target tracking performance is about twice the one millidegree objective, with several factors contributing. The first factor is the bias stability of the gyros, which is exceptional, but will limit performance to the 0.001 and 0.002 degree range for long tracking periods. The second contributing factor is the accelerometer contributions when the system is elevated. These degrade performance into the 0.003 to 0.004 degree range, which could be improved upon with some additional changes. Finally, we have provided a set of recommendations to improve performance closer to the technical objectives. These recommendations include gyro, electronics, and system configurational changes that form the basis for additional work to achieve the desired performance. In conclusion, we believe that the RL-34 ring laser gyro-based advanced navigation system demonstrated performance consistent with expectations and technical objectives, and it has the potential for even further enhancement for the DSN application.
Source record↗
UAS Conflict-Avoidance Using Multiagent RL with Abstract Strategy Type Communication
The use of unmanned aerial systems (UAS) in the national airspace is of growing interest to the research community. Safety and scalability of control algorithms are key to the successful integration of autonomous system into a human-populated airspace. In order to ensure safety while still maintaining efficient paths of travel, these algorithms must also accommodate heterogeneity of path strategies of its neighbors. We show that, using multiagent RL, we can improve the speed with which conflicts are resolved in cases with up to 80 aircraft within a section of the airspace. In addition, we show that the introduction of abstract agent strategy types to partition the state space is helpful in resolving conflicts, particularly in high congestion.
Unmanned Autonomous Systems↗
Announced Strategy Types in Multiagent RL for Conflict-Avoidance in the National Airspace
The use of unmanned aerial systems (UAS) in the national airspace is of growing interest to the research community. Safety and scalability of control algorithms are key to the successful integration of autonomous system into a human-populated airspace. In order to ensure safety while still maintaining efficient paths of travel, these algorithms must also accommodate heterogeneity of path strategies of its neighbors. We show that, using multiagent RL, we can improve the speed with which conflicts are resolved in cases with up to 80 aircraft within a section of the airspace. In addition, we show that the introduction of abstract agent strategy types to partition the state space is helpful in resolving conflicts, particularly in high congestion.
National Airspace↗
Comparison Between CCCM and CloudSat Radar-Lidar (RL) Cloud and Radiation Products
To enhance cloud properties, LaRC and CIRA developed each combination algorithm for obtained properties from passive, active and imager in A-satellite constellation. When comparing global cloud fraction each other, LaRC-produced CERES-CALIPSO-CloudSat-MODIS (CCCM) products larger low-level cloud fraction over tropic ocean, while CIRA-produced Radar-Lidar (RL) shows larger mid-level cloud fraction for high latitude region. The reason for different low-level cloud fraction is due to different filtering method of lidar-detected cloud layers. Meanwhile difference in mid-level clouds is occurred due to different priority of cloud boundaries from lidar and radar.
Ham, Seung-Hee↗
Stress relaxation and mechanical properties of RL-1973 and PD-200-16 silicone resin sponge materials
Stress relaxation tests were conducted by loading specimens in double-lap shear to a preselected strain level and monitoring the decay of stress with time. The stress relaxation response characteristics were measured over a temperature range of 100 to 300 K and four strain levels. It is concluded that only a slight amount of stress relaxation was observed, and the stiffness increased approximately two orders of magnitude over the range of temperatures.
Saylak, D.↗
RL-10 Based Combined Cycle For A Small Reusable Single-Stage-To-Orbit Launcher
This paper discusses a new application of the combined propulsion known as the KLIN(TM) cycle, consisting of a thermally integrated deeply cooled turbojet (DCTJ) and liquid rocket engine (LRE). If based on the RL10 rocket engine family, the KLIN (TM) cycle makes a small single-stage-to-orbit (SSTO) reusable launcher feasible and economically very attractive. Considered in this paper are the concept and parameters of a small SSTO reusable launch vehicle (RLV) powered by the KLIN (TM) cycle (sSSTO(TM)) launcher. Also discussed are the benefits of the small launcher, the reusability, and the combined cycle application. This paper shows the significant reduction of the gross take off weight (GTOW) and dry weight of the KLIN(TM) cycle-powered launcher compared to an all-rocket launcher.
Balepin, Vladimir↗
SAN-RL: combining spreading activation networks and reinforcement learning to learn configurable behaviors
This approach provides the agent with a causal structure, the spreading activation network, relating goals to the actions that can achieve those goals. This enables the agent to select actions relative to the goal priorities.
bevavior-based robotics planning machine learning↗
Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling
Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.
reinforcement learning↗
Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling
Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.
Validation↗
Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling
Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.
Validation↗
Deep RL for Fast Long-Horizon Operations Scheduling on NASA's Carruthers Geocorona Observatory Mission
Spacecraft operations scheduling is a highly constrained, long-horizon combinatorial optimization problem that traditionally relies on heuristics, constraint programming, or manual planning. We present a scalable deep reinforcement learning framework developed and deployed for NASA’s Carruthers Geocorona Observatory mission. Our framework introduces a macro-action abstraction known as activity blocks coupled with dynamic action-masking to navigate the intractably large search space and strictly enforce complex power, thermal, and instrument constraints. The resulting architecture generates globally feasible schedules with overwhelming probability, establishes operational trust, and executes a full training cycle in under six hours, circumventing the need for policy robustness by enabling rapid, on-demand retraining. Further, resulting schedules outperform baseline heuristics in scheduled science quality. The deep reinforcement learning framework was deployed as the default operational scheduler for the Carruthers Geocorona Observatory mission from the outset of the mission, demonstrating that deep reinforcement learning can be trusted for real spacecraft operations under complex, evolving constraints.
Geocorona↗
Photoinhibition of stem elongation by blue and red light: effects on hydraulic and cell wall properties
The underlying mechanism of photoinhibition of stem elongation by blue (BL) and red light (RL) was studied in etiolated seedlings of pea (Pisum sativum L. cv Alaska). Brief BL irradiations resulted in fast transient inhibition of elongation, while a delayed (lag approximately 60 minutes) but prolonged inhibition was observed after brief RL. Possible changes in the hydraulic and wall properties of the growing cells during photoinhibition were examined. Cell sap osmotic pressure was unaffected by BL and RL, but both irradiations increased turgor pressure by approximately 0.05 megapascal (pressure-probe technique). Cell wall yielding was analyzed by in vivo stress relaxation (pressure-block technique). BL and RL reduced the initial rate of relaxation by 38 and 54%, while the final amount of relaxation was decreased by 48 and 10%, respectively. These results indicate that RL inhibits elongation mainly by lowering the wall yield coefficient, while most of the inhibitory effect of BL was due to an increase of the yield threshold. Mechanical extensibility of cell walls (Instron technique) was decreased by BL and RL, mainly due to a reduction in the plastic component of extensibility. Thus, photoinhibitions of elongation by both BL and RL are achieved through changes in cell wall properties, and are not due to effects on the hydraulic properties of the cell.
NASA Program Space Biology↗
SoMoGym: A Toolkit for Developing and Evaluating Controllers and Reinforcement Learning Algorithms for Soft Robots
Soft robotsoffer a host of benefits over traditional rigid robots, including inherent compliance that lets them passively adapt to variable environments and operate safely around humans and fragile objects. However, that same compliance makes it hard to use model-based methods in planning tasks requiring high precision or complex actuation sequences. Reinforcement learning (RL) can potentially find effective control policies, but training RL using physical soft robots is often infeasible, and training using simulations has had a high barrier to adoption. To accelerate research in control and RL for soft robotic systems, we introduce SoMoGym ( So ft Mo tion Gym ), a software toolkit that facilitates training and evaluating controllers for continuum robots. SoMoGym provides a set of benchmark tasks in which soft robots interact with various objects and environments. It allows evaluation of performance on these tasks for controllers of interest, and enables the use of RL to generate new controllers. Custom environments and robots can likewise be added easily. We provide and evaluate baseline RL policies for each of the benchmark tasks. These results show that SoMoGym enables the use of RL for continuum robots, a class of robots not covered by existing benchmarks, giving them the capability to autonomously solve tasks that were previously unattainable.
Moritz A. Graule↗
Interplanetary Shocks Lacking Type 2 Radio Bursts
We report on the radio-emission characteristics of 222 interplanetary (IP) shocks detected by spacecraft at Sun-Earth L1 during solar cycle 23 (1996 to 2006, inclusive). A surprisingly large fraction of the IP shocks (approximately 34%) was radio quiet (RQ; i.e., the shocks lacked type II radio bursts). We examined the properties of coronal mass ejections (CMEs) and soft X-ray flares associated with such RQ shocks and compared them with those of the radio-loud (RL) shocks. The CMEs associated with the RQ shocks were generally slow (average speed approximately 535 km/s) and only approximately 40% of the CMEs were halos. The corresponding numbers for CMEs associated with RL shocks were 1237 km/s and 72%, respectively. Thus, the CME kinetic energy seems to be the deciding factor in the radio-emission properties of shocks. The lower kinetic energy of CMEs associated with RQ shocks is also suggested by the lower peak soft X-ray flux of the associated flares (C3.4 versus M4.7 for RL shocks). CMEs associated with RQ CMEs were generally accelerating within the coronagraph field of view (average acceleration approximately +6.8 m/s (exp 2)), while those associated with RL shocks were decelerating (average acceleration approximately 3.5 m/s (exp 2)). This suggests that many of the RQ shocks formed at large distances from the Sun, typically beyond 10 Rs, consistent with the absence of metric and decameter-hectometric (DH) type II radio bursts. A small fraction of RL shocks had type II radio emission solely in the kilometric (km) wavelength domain. Interestingly, the kinematics of the CMEs associated with the km type II bursts is similar to those of RQ shocks, except that the former are slightly more energetic. Comparison of the shock Mach numbers at 1 AU shows that the RQ shocks are mostly subcritical, suggesting that they were not efficient in accelerating electrons. The Mach number values also indicate that most of these are quasi-perpendicular shocks. The radio-quietness is predominant in the rise phase and decreases through the maximum and declining phases of solar cycle 23. About 18% of the IP shocks do not have discernible ejecta behind them. These shocks are due to CMEs moving at large angles from the Sun-Earth line and hence are not blast waves. The solar sources of the shock-driving CMEs follow the sunspot butterfly diagram, consistent with the higher-energy requirement for driving shocks.
Gopalswamy, N.↗
Nonlinear evolution of high frequency R-mode waves excited by water group ions near comets - Computer experiments
An ion beam resonates with R-mode waves at a high-frequency RH mode and a low-frequency RL mode. The nonlinear evolution of ion beam-generated RH waves is studied here by one-dimensional hybrid computer experiments. Both wave-particle and subsequent wave-wave interactions are examined. The competing process among coexisting RH and RL mode beam instabilities and repeated decay instabilities triggered by the beam-excited RH mode waves is clarified. It is found that the quenching of the RH instability is not caused by a thermal spreading of the ion beam, but by the nonlinear wave-wave coupling process. The growing RH waves become unstable against the decay instability. This instability involves a backward-traveling RH electromagnetic wave and a forward-traveling longitudinal sound wave. The inverse cascading process is found to occur faster than the growth of the RL mode. Wave spectra decaying from the RH waves weaken as time elapses and the RL mode waves become dominant at the end of the computer experiment.
Kojima, H.↗
Dynamic Altitude Simulation System Performance Modeling
The "Dynamic Altitude Simulation Prediction Program," DASSPP, is a program to predict the transient response of an engine, test cell, ejector system under engine shutdown conditions. These transients are important to know so that corrective modifications can be adapted to prevent any damage to the engine or test cell. The "Dynamic Altitude System Simulation Prediction Program," DASSPP, is a major rewrite of the existing program "RL-1000" written in BASIC. The RL-1000 program was written to analyze the transients of the RL-10 system only. The new program is written to run in Excell 97 and utilizes the Visual BASIC language in Excell. The program has many added features not included in the original "Rl-1000" program. The program utilizes the ejector models developed during the summer of 1997. The new program is very user friendly and utilizes a dialog box for data input.
LaFrance, Leo J.↗