The dynamic environment simulator as a robotic system: kinematic issues

: This report describes all SBIR Phase 2 effort to validate a mathematical model mid conduct a design study of the Air Force Dynamic Environment The Dynamic Environment Simulator, or DES, is a three degree-of-freedom centrifuge operated by Armstrong Laboratories at Wright-Patterson AFB. The effort was conducted under Contract F41624-96-C-6027 through the Armstrong Laboratory and was entitled Model Validation and Design Study of the Dynamic Environment Simulator. This Phase 2 project had four primary objectives, which were to: (1) Validate the Phase 1 software model of the DES; (2) Conduct a design study of DES improvements; (3) Perform a cost/benefit analysis of the possible DES improvements; and (4) Demonstrate the commercial potential of the model and the DES.

The ability to localize a virtual auditory source was evaluated under varying levels of sustained (+G2) acceleration. Participants were required to judge the locations of virtual auditory cues located along the horizontal plane (elevation 0°) during exposure to 1.0, 1.6, 2.5, 4.0, 5.6, and 7.0 +Gz. The experiment was conducted at the Air Force Research Laboratory's Dynamic Environment Simulator - a man-rated, three-axis centrifuge. No significant increases in localization error were found between 1.0 and 5.6 +Gz; however, a significant increase did occur at the 7.0 +Gz level. In addition, the percentage of reversals did not vary as a function of +Gz level. Collectively, these results indicate that one's ability to localize virtual auditory cues is well maintained at various levels of sustained acceleration.

This project is part of a research effort to design a hydrogen plant and its interface with a nuclear reactor. This project developed a dynamic modeling, simulation and optimization environment for nuclear hydrogen production systems. A hybrid discrete/continuous model captures both the continuous dynamics of the nuclear plant, the hydrogen plant, and their interface, along with discrete events such as major upsets. This hybrid model makes us of accurate thermodynamic sub-models for the description of phase and reaction equilibria in the thermochemical reactor. Use of the detailed thermodynamic models will allow researchers to examine the process in detail and have confidence in the accurary of the property package they use.

Differentiating responses of weather files and local climate change to explain variations in building thermal-energy performance simulations

In this work a biologically inspired network of spiking neurons is used for robot navigation control. The two tasks taken into account are obstacle avoidance and landmark-based navigation. The system learns the correlation among unconditioned stimuli (pre-wired sensors) and conditioned stimuli (high level sensors) through Spike Timing Dependent Plasticity (STDP). In order to improve the robot behaviours not only the synaptic weight but also the synaptic delay is subject to learning. Modulating the synaptic delay the robot is able to store the landmark position, like in a short time memory, and to use this information to smooth the turning actions prolonging the landmark effects also when it is no more visible. Simulations are carried out in a dynamic simulation environment and the robotic system considered is a cockroach-inspired hexapod robot. The locomotion signals are generated by a Central Pattern Generator and the spiking network is devoted to control the heading of the robot acting on the amplitude of the leg steps. Several scenarios have been proposed, for instance a T-shaped labyrinth, used in laboratory experiments with mice to demonstrate classical and operant conditioning, has been considered. Finally the proposed adaptive navigation control structure can be extended in a modular way to include other features detected by new sensors included in the correlation-based learning process.

: Females are now flying combat aircraft in the Air Force. Questions Of gender differences regarding adaptation and performance)in the high environment (9G) must be studied Scientifically. The Dynamic Environment Simulator, a three- axis centrifuge with closed-loop flight simulation, provides the laboratory to investigate the issues. Results. The eight women in this high-G performance study did not show cardiovascular adaptation to high whereas the eight men did. Both genders showed increased leg calf compliance indicating possible chronic vascular effects. No echocardiographic graphic evidence of heart damage was found The women demonstrated half the strength of the men, but displayed similar G tolerance and endurance. The women showed less oxygen desaturation of brain tissue than the men during high exposure. The women did not perform the simulated air-to-air combat sortie quite as well as the men, though there was no effect of menstrual cycle on their ability to complete the mission There was no effect of high exposure on the length Or the female monthly cycle, regardless of oral contraceptive use. Conclusion. Women demonstrated acceptable tolerance to and performance during simulated high aerial combat, without menstrual effect, even in light of their reduced muscular strength and cardiovascular adaptation as compared to men.

A colored traveling salesman problem (CTSP) is a path optimization problem in which colors are used to characterize diverse matching relationship between cities and salesmen. Namely, each salesman has a single color while every city has one to multiple salesmen’s colors, thus allowing salesmen to visit exactly once the cities of their colors. It is noteworthy that cities’ accessibilities to salesmen may change over time, which usually takes place in the multiwarehouse distribution of online retailers. This work presents a new CTSP with dynamically varying city colors for describing and modeling some scheduling problems with variable city accessibilities. The problem is more complicated than the previously proposed CTSP with varying edge weights. In particular, the solution feasibility changes as the cities change their colors, that is, a feasible original solution path may become no longer feasible after city colors change. A variable neighborhood search (VNS) algorithm is presented to solve the new problem. Specifically, a dynamic environment simulator with an adjustable frequency and amplitude is designed to mimic such color changes. Then, direct-route encoding, greedy initialization, and appropriate population immigrant are proposed to form an enhanced VNS, and then its performance is evaluated. The results of extensive experiments show that the proposed VNS can quickly track the environmental changes and effectively resolve the problem.

: Twenty-two student test pilots have participated in closed-loop tracking indoctrination runs on the Dynamic Environment Simulator. Each pilot flew through a series of profiles up to 6 G in both the conventional upright seat position and a 55 degrees tilt back seat position. The subjective responses and pilot questionnaires are presented.

When moving objects that are too bulky or heavy to be grasped or lifted, robotic manipulation can benefit from the object's interaction with the support surface and its natural dynamics under gravity. In this work, we show that such dynamic, underactuated manipulation capability can be acquired through reinforcement learning and deployed on real robot systems. First, we present a framework to learn a control policy for object transport in a dynamic simulation environment, featuring the object and the support surface. We then demonstrate successful object locomotion with the learned policy through a set of simulated and real-world experiments, performed with a robot arm and an aerial robot interacting with the object in a non-prehensile manner. While the object, which is in contact with the support surface, oscillates sideways passively under gravity, the robot uses the learned policy to move the object forward with a steady gait by regulating the mechanical energy and the posture of the object. Our experiment results show that the learned policy can transport the object through unmodeled effects of terrain and perturbation.

Object-oriented components for dynamic hybrid simulation of a reactive distillation process

This paper demonstrates the concept of probabilistic stability assessment on large-signal stability in the use case of short circuits in an active distribution grid. Here, the concept of survivability is applied, which extends classical stability assessments by evaluating the stability and operational limits during transients for a wide range of operating points and failures. For this purpose, a free, open-source, and computationally efficient environment (Julia) for dynamic simulation of power grids is used to demonstrate its capabilities. The model implementation is validated against established commercial software and deviations are minimal with respect to power flow and dynamic simulations. The results of a large-scale survivability analysis reveal i) a broad field of application for probabilistic stability analysis and ii) that new non-intuitive stability correlations can be obtained. Hence, the proposed method shows strong potential to efficiently conduct power system stability analysis in active distribution grids.

The problem of distance computation arises in many applications including motion planning, CAD/CAM, dynamic simulation and virtual environments. Most prior work in this area has been restricted to separation or penetration distance computation between two objects. In this paper, we address the problem of computing a measure of distance between two configurations of a rigid or articulated model. The underlying distance metric is defined as the length of the longest displacement vector over the corresponding vertices of the model between two configurations. Our algorithm is based on Chasles theorem in Screw theory, and we show that the maximum distance can be realized only by a vertex of the convex hull of a rigid object. We use this formulation to compute the distance, and present two acceleration techniques to speed up the computation: incremental walking on the dual space of the convex hull and culling vertices on the convex hull using a bounding volume hierarchy (BVH). Our algorithm can be easily extended to articulated models by maximizing the distance over its each link and we also present culling techniques to accelerate the computation. We highlight the performance of our algorithm on many complex models and describe its application to proximity queries and motion planning.

In this paper, the locomotion and steering control of a simulated Mini Cheetah quadruped robot was investigated in the presence of terrain characterised by low friction. Low-level locomotion and steering control were implemented via a central pattern generator approach, whereas high-level steering control manoeuvres were implemented by comparing a neural network and a linear model predictive controller in a dynamic simulation environment. A data-driven approach was adopted to identify the robot model using both a linear transfer function and a shallow artificial neural network. The results demonstrate that, whereas the linear approach showed good performance in high-friction terrain, in the presence of slippery conditions, the application of a neural network predictive controller improved trajectory accuracy and preserved robot safety with different steering manoeuvres. A comparative analysis was carried out using several performance indices.

The key to the successful "factory of the future" will be the effective coordination of managerial functions and production operations. The research presented in this dissertation addresses this "coordination" issue, especially in terms of understanding the impact and possibilities which exist in an information-rich environment. Such an environment is often referred to in the literature as being a computer-integrated manufacturing (CIM) environment. An interactive simulation modeling program, MOSES (Manufacturing Organization Simulation and Evaluation System), was developed for the purpose of describing, analyzing, and understanding such manufacturing. MOSES is a copyrighted computer program, a menu-driven, interactive, simulation modeling tool. MOSES users are able to define the manufacturing environment to be simulated by keying in data values (e.g., products, bills of material, and production rates) which describe the organization. MOSES then uses these data values to "build the manufacturing simulation model." That is, once the manufacturing parameters have been defined, MOSES can simulate the manufacturing organization (from orders through distribution) without the user having to write any computer code or having to understand the principles of simulation modeling. MOSES users are then able to interact with the manufacturing organization as "managers" in the dynamic simulation environment. This interactive capability allows MOSES users to test various manufacturing scenarios, watch the model's progress from the perspectives of different organizational functions (e.g., from marketing or from production), override model-generated decisions (e.g., by changing the demand forecast), and view the results. The MOSES approach to simulation modeling differs from traditional approaches. In short, MOSES uses both mathematic and logical modeling principles, employs traditional simulation modeling techniques but in combination with database technology, and includes the user as an integral component in the simulation process.

• Developed dynamic models to simulate LFR-based nuclear propulsion systems. • Introduced the Ship-to-Shore concept, enabling clean energy transfer to ports. • Achieved significant CO 2 emission reduction: 24.5 kt/y during navigation. • Increased energy production by 697% through the Ship-to-Shore concept. • Provided a pathway for sustainable maritime propulsion aligned with IMO targets. This paper investigates the integration of IV generation small modular reactors into maritime vessels as a viable alternative to conventional fossil fuel-powered systems. To support maritime decarbonisation and evaluate the energy and economic performance of their onboard implementation, suitable mathematical models of energy systems powered by small modular reactors are developed. Among various nuclear technologies, a lead-cooled fast reactor (LFR) using mixed oxide fuel is modelled and implemented within a dynamic simulation environment. The model simulates the energy performance of a Helium Brayton cycle with a regenerator and reheating, powered by an LFR. This model can assess the temperature distribution of the lead coolant, the helium temperatures throughout the Brayton cycle, the generated mechanical and electrical power, thermodynamic efficiency, and waste heat. Following the development of the LFR-based model, it is integrated into a broader computational framework that enables the evaluation of the ship’s overall energy performance, including the energy demands of onboard utilities under different operating modes and climatic conditions. To demonstrate the potential of these systems, a LFR is simulated as a replacement for conventional fossil fuel engines onboard a containership. A representative route, reconstructed from real-time online data, is used to assess reactor performance under dynamic maritime conditions. Furthermore, a novel concept – ship-to-shore connection – is introduced, whereby surplus electricity produced by the onboard LFR is supplied to port grids, contributing to the decarbonisation of port facilities and surrounding areas. Simulation results indicate that small modular reactor systems could effectively nullify onboard fossil fuel consumption. For instance, avoided CO 2 emissions are estimated at 24.5 kt/y during navigation and 15.3 kt/y during port stays, including also emissions offset through the replacement of national thermoelectric power generation in ship-to-shore application. The implementation of the ship-to-shore connection concept results in an increase in energy production during port stays ranging from 572% to 697%, yielding substantial environmental and economic benefits.