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While the general class of most scheduling problems is NP-hard in worst-case complexity, in practice, for specific distributions of problems and constraints, domain-specific solutions have been shown to perform in much better than exponential time.
The ground based electro-optical deep space surveillance program involves a network of computer controlled 40 inch 1m telescopes equipped with large format, low light level, television cameras of the intensified silicon diode array type which is to replace the Baker-Nunn photographic camera system for artificial satellite tracking. A prototype observatory was constructed where distant artificial satellites are discriminated from stars in real time on the basis of the satellites' proper motion. Hardware was modified and the technique was used to observe and search for minor planets. Asteroids are now routinely observed and searched. The complete observing cycle, including the 2"-3" measurement of position, requires about four minutes at present. The commonality of asteroids and artificial satellite observing, searching, data reduction, and orbital analysis is stressed. Improvements to the hardware and software as well as operational techniques are considered.
Abstract Several applications in the scientific simulation of physical systems can be formulated as control/optimization problems. The computational models for such systems generally contain hyperparameters, which control solution fidelity and computational expense. The tuning of these parameters is non-trivial and the general approach is to manually ‘spot-check’ for good combinations. This is because optimal hyperparameter configuration search becomes intractable when the parameter space is large and when they may vary dynamically. To address this issue, we present a framework based on deep reinforcement learning (RL) to train a deep neural network agent that controls a model solve by varying parameters dynamically. First, we validate our RL framework for the problem of controlling chaos in chaotic systems by dynamically changing the parameters of the system. Subsequently, we illustrate the capabilities of our framework for accelerating the convergence of a steady-state computational fluid dynamics solver by automatically adjusting the relaxation factors of the discretized Navier–Stokes equations during run-time. The results indicate that the run-time control of the relaxation factors by the learned policy leads to a significant reduction in the number of iterations for convergence compared to the random selection of the relaxation factors. Our results point to potential benefits for learning adaptive hyperparameter learning strategies across different geometries and boundary conditions with implications for reduced computational campaign expenses 4 4 Data and codes available at https://github.com/Romit-Maulik/PAR-RL . .
Wide adoption of deep reinforcement learning need to overcome several challenges in energy system domain, including scalability, learning from limited samples, and high-dimensional continuous state and action spaces. In this paper, we integrated physics-based information from the normal generator operation state formula in the reinforcement learning agent's neural network loss function, and applied an augmented random search agent to optimize the generator control under dynamic contingency. Simulation results demonstrated the reliability performance improvements in training speed, reward convergence, sampling efficiency, scalability, and transferability.
A coherent uplink array consisting of up to three 34-meter antennas of NASA's Deep Space Network has been developed for the primary purpose of increasing EIRP at the spacecraft. Greater EIRP ensures greater reach, higher uplink data rates for command and configuration control, as well as improved search and recovery capabilities during spacecraft emergencies. It has been conjectured that Doppler-delay radar imaging of lunar targets can be extended to planetary imaging, where the long baseline of the uplink array can provide greater resolution than a single antenna, as well as potentially higher EIRP. However, due to the well known R-4 loss in radar links, imaging of distant planets is a very challenging endeavor, requiring accurate phasing of the Uplink Array antennas, cryogenically cooled low-noise receiver amplifiers, and sophisticated processing of the received data to extract the weak echoes characteristic of planetary radar. This article describes experiments currently under way to image the planets Mercury and Venus, highlights improvements in equipment and techniques, and presents planetary images obtained to date with two 34 meter antennas configured as a coherently phased Uplink Array.
A coherent Uplink Array consisting of two or three 34-meter antennas of NASA's Deep Space Network has been developed for the primary purpose of increasing EIRP at the spacecraft. Greater EIRP ensures greater reach, higher uplink data rates for command and configuration control, as well as improved search and recovery capabilities during spacecraft emergencies. It has been conjectured that Doppler-delay radar imaging of lunar targets can be extended to planetary imaging, where the long baseline of the uplink array can provide greater resolution than a single antenna, as well as potentially higher EIRP. However, due to the well known R4 loss in radar links, imaging of distant planets is a very challenging endeavor, requiring accurate phasing of the Uplink Array antennas, cryogenically cooled low-noise receiver amplifiers, and sophisticated processing of the received data to extract the weak echoes characteristic of planetary radar. This article describes experiments currently under way to image the planets Mercury and Venus, highlights improvements in equipment and techniques, and presents planetary images obtained to date with two 34 meter antennas configured as a coherently phased Uplink Array.
Activities in space communication, radio navigation, radio science, and ground-based astronomy are reported. Advanced systems for the Deep Space Network and its Ground-Communications Facility are discussed including station control and system technology. Network sustaining as well as data and information systems are covered. Studies of geodynamics, investigations of the microwave spectrum, and the search for extraterrestrial intelligence are reported.
The 1975-1976 Viking Mars Mission is described in detail, from launch phase through landing and communications relay phase. The mission's scientific goals are outlined and the various Martian investigations are discussed. These investigations include: geological photomapping and seismology; high-resolution, stereoscopic horizon scanning; water vapor and thermal mapping; entry science; meteorology; atmospheric composition and atmospheric density; and, search for biological products. The configurations of the Titan 3/Centaur combined launch vehicles, the Viking orbiters, and the Viking landers are described; their subsystems and performance characteristics are discussed. Preflight operations, launch window, mission control, and the deep space tracking network are also presented.
Archival reports on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition (TDA) are provided. In space communications, radio navigation, radio science, and ground-based radio and radar astronomy, it reports on activities of the Deep Space Network (DSN) in planning, in supporting research and technology, in implementation, and in operations. Also included is standards activity at JPL for space data and information. In the search for extraterrestrial intelligence (SETI), the TDA Progress Report reports on implementation and operations for searching the microwave spectrum. Topics covered include tracking and ground-based navigation; communications, spacecraft-ground; station control and system technology; capabilities for new projects; network upgrade and sustaining; network operations and operations support; and TDA program management and analysis.
An overview is presented of major new requirements, challenges and conceptual designs for the DSN Radio Science System in the 1985 to 1988 period. The Voyager Uranus encounter is being supported with larger combined aperture, higher sample rate, and a centrally controlled network. The Galileo mission will be provided with a high resolution S-Band Faraday rotation detection capability and a high-stability Doppler system with X-Band uplink for gravitational wave search.
Deep neural networks have been successfully employed in an extensive variety of research areas, including solving partial differential equations. Despite its significant success, there are some challenges in effectively training DNN, such as avoiding overfitting in over-parameterized DNNs and accelerating the optimization in DNNs with pathological curvature. Here, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hierarchical Bayesian mixture will be trained using an adaptive empirical method. That is, one will alternatively sample from the posterior using preconditioned stochastic gradient Langevin Dynamics (PSGLD), and optimize the latent variables via stochastic approximation. The sparsity of the network is achieved while optimizing the hyperparameters with adaptive searching and penalizing. A popular SG-MCMC approach is Stochastic gradient Langevin dynamics (SGLD). However, considering the complex geometry in the model parameter space in nonconvex learning, updating parameters using a universal step size in each component as in SGLD may cause slow mixing. To address this issue, we apply a computationally manageable preconditioner in the updating rule, which provides a step-size parameter to adapt to local geometric properties. Moreover, by smoothly optimizing the hyperparameter in the preconditioning matrix, our proposed algorithm ensures a decreasing bias, which is introduced by ignoring the correction term in the preconditioned SGLD. According to the existing theoretical framework, we show that the proposed algorithm can asymptotically converge to the correct distribution with a controllable bias under mild conditions. Numerical tests are performed on both synthetic regression problems and learning solutions of elliptic PDE, which demonstrate the accuracy and efficiency of the present work.
Machine learning (ML) is growing in popularity for various particle accelerator applications including anomaly detection such as faulty beam position monitor or RF fault identification, for non-invasive diagnostics, and for creating surrogate models. ML methods such as neural networks (NN) are useful because they can learn input-output relationships in large complex systems based on large data sets. Once they are trained, methods such as NNs give instant predictions of complex phenomenon, which makes their use as surrogate models especially appealing for speeding up large parameter space searches which otherwise require computationally expensive simulations. However, quickly time varying systems are challenging for ML-based approaches because the actual system dynamics quickly drifts away from the description provided by any fixed data set, degrading the predictive power of any ML method, and limits their applicability for real time feedback control of quickly time-varying accelerator components and beams. In contrast to ML methods, adaptive model-independent feedback algorithms are by design robust to un-modeled changes and disturbances in dynamic systems, but are usually local in nature and susceptible to local extrema. In this work, we propose that the combination of adaptive feedback and machine learning, adaptive machine learning (AML), is a way to combine the global feature learning power of ML methods such as deep neural networks with the robustness of model-independent control. We present an overview of several ML and adaptive control methods, their strengths and limitations, and an overview of AML approaches.
This paper is a new step in the development and application of the Linguistic Geometry. This formal theory is intended to discover the inner properties of human expert heuristics, which have been successful in a certain class of complex control systems, and apply them to different systems. In this paper we investigate heuristics extracted in the form of hierarchical networks of planning paths of autonomous agents. Employing Linguistic Geometry tools the dynamic hierarchy of networks is represented as a hierarchy of formal attribute languages. The main ideas of this methodology are shown in this paper on the new pilot example of the solution of the extremely complex 3D optimization problem of strategic planning for the space combat of autonomous vehicles. This example demonstrates deep and highly selective search in comparison with conventional search algorithms.
A portable comprehensive navigational system has been developed that both robotic and human explorers can use to determine their location, attitude, and heading anywhere on the lunar surface independent of external infrastructure (needs no Lunar satellite network, line of sight to the Sun or Earth, etc.). The system combines robust processing power with an extensive topographical database to create a real-time atlas (GIS Geospatial Information System) that is able to autonomously control and monitor both single unmanned rovers and fleets of rovers, as well as science payload stations. The system includes provisions for teleoperation and tele-presence. The system accepts (but does not require) inputs from a wide range of sensors. A means was needed to establish a location when the search is taken deep in a crater (looking for water ice) and out of view of Earth or any other references. A star camera can be employed to determine the user's attitude in menial space and stellar map in body space. A local nadir reference (e.g., an accelerometer that orients the nadir vector in body space) can be used in conjunction with a digital ephemeris and gravity model of the Moon to isolate the latitude, longitude, and azimuth of the user on the surface. That information can be used in conjunction with a Lunar GIS and advanced navigation planning algorithms to aid astronauts (or other assets) to navigate on the Lunar surface.
The DSN Scheduling Engine (DSE) has been developed to increase the level of automated scheduling support available to users of NASA s Deep Space Network (DSN). We have adopted a request-driven approach to DSN scheduling, in contrast to the activity-oriented approach used up to now. Scheduling requests allow users to declaratively specify patterns and conditions on their DSN service allocations, including timing, resource requirements, gaps, overlaps, time linkages among services, repetition, priorities, and a wide range of additional factors and preferences. The DSE incorporates a model of the key constraints and preferences of the DSN scheduling domain, along with algorithms to expand scheduling requests into valid resource allocations, to resolve schedule conflicts, and to repair unsatisfied requests. We use time-bounded systematic search with constraint relaxation to return nearby solutions if exact ones cannot be found, where the relaxation options and order are under user control. To explore the usability aspects of our approach we have developed a graphical user interface incorporating some crucial features to make it easier to work with complex scheduling requests. Among these are: progressive revelation of relevant detail, immediate propagation and visual feedback from a user s decisions, and a meeting calendar metaphor for repeated patterns of requests. Even as a prototype, the DSE has been deployed and adopted as the initial step in building the operational DSN schedule, thus representing an important initial validation of our overall approach. The DSE is a core element of the DSN Service Scheduling Software (S(sup 3)), a web-based collaborative scheduling system now under development for deployment to all DSN users.
This paper presents a collaborative conflict resolution technique using deep neural network-based intelligent search of the solution space. This approach offers a rapid convergence to a mutually acceptable solution for real-time conflict resolution, suitable for urban air mobility operations. Furthermore, the presented technique allows operational flexibility to the urban air mobility agents where these agents can collaboratively devise the solution via integrative negotiation, based on their local utility functions, as long as such a solution does not violate the global safety thresholds. The presented machine-to-machine negotiation method is built on our prior work on holistic assessment of the airspace and potential conflict detection implemented at-the-edge, onboard the unmanned aircraft systems. This paper extends the prior work to augment decision-making at-the-edge, thereby, promising a true distributed control architecture for urban air mobility. In this approach, each agent (a) builds a potential in-flight conflict map, (b) identifies the conflicting agents, (c) dynamically prepares a list of alternatives based on its current utility functions, (d) negotiates with the conflicting agents to pick one of these alternatives, and (e) implements the negotiated alternative to mutually resolve the conflict. Note that such an approach does not require a contingency plan to be made pre-flight, as the conflict resolution strategies are decided and negotiated in real time based on the present state of the agent. The contingency plan, if available, can serve as an input to the real-time conflict resolution strategy formulation, and also can be used as a fallback plan in case the negotiation fails and the impacted agents need to switch to a rule-based/supervisory resolution mode from the discussed distributed resolution mode. The presented collaborative negotiation-based conflict resolution technique incorporates a time-dependent reward function to catalyze collaborative resolution by incentivizing the agents with local and global rewards beneficial to their business operations.
Steve Wall is the host of this video entitled, "Return to the Red Planet". Live animation of the Mars Observer orbiting Mars is presented. Steve Wall explains the spacecraft insertion maneuver and also explains the purpose for the Mars Observer launch. Live coverage of the Cape Canaveral launch of the Mars Observer is also presented. Suzanne Dodd, Chief of the Mission Planning team describes the burn start and how the spacecraft will be captured by Mars' gravity. Glenn Cunningham, Mars Observer Project Manager, gives background information on the Mars Observer and describes the organizations behind the Mars Observer Spacecraft, such as the Deep Space Network, the Mission Operation Support Office, Science Investigators, the Flight Engineering Office, Operations Office, and the Ground Data System Office. Dr. William Piotrowski, Acting Director, Solar System Exploration Division, NASA, talks about the purpose of the Mars Pathfinder which is to develop the technology and systems for landing small science packages on Mars. Mr. Roger Gibbs, Former Mars Observer Spacecraft Systems Engineer, tells us how the Mars Observer was built and describes the structural elements on the Mars Observer. The 11-month cruise period for the spacecraft is given by Joseph Beerer, Manager of the Engineering office. The thrust for the Mars Orbit Insertion is described by Ronald Klemetson, Technical Manager, Propulsion Subsystem Jet Propulsion Laboratory (JPL). George Chen, Lead Engineer Attitude and Articulation Subsystem Spacecraft Team, explains the importance of the attitude control engines on the Spacecraft. Marvin Traxler, Manager of Tracking and Data Acquisition, describes how searching for a signal from the Mars Observer works. See NONP-NASA-VT-2000081555 for a continuation of this discussion with Marvin Traxler.
Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action spaces, as in chess, Shogi and Go. Many real-world materials discovery and design applications, however, involve multi-dimensional search problems and learning domains that have continuous action spaces. Exploring high-dimensional potential energy models of materials is an example. Traditionally, these searches are time consuming (often several years for a single bulk system) and driven by human intuition and/or expertise and more recently by global/local optimization searches that have issues with convergence and/or do not scale well with the search dimensionality. Here, in a departure from discrete action and other gradient-based approaches, we introduce a RL strategy based on decision trees that incorporates modified rewards for improved exploration, efficient sampling during playouts and a “window scaling scheme" for enhanced exploitation, to enable efficient and scalable search for continuous action space problems. Using high-dimensional artificial landscapes and control RL problems, we successfully benchmark our approach against popular global optimization schemes and state of the art policy gradient methods, respectively. We demonstrate its efficacy to parameterize potential models (physics based and high-dimensional neural networks) for 54 different elemental systems across the periodic table as well as alloys. We analyze error trends across different elements in the latent space and trace their origin to elemental structural diversity and the smoothness of the element energy surface. Broadly, our RL strategy will be applicable to many other physical science problems involving search over continuous action spaces.