Analytic platform simulation
Analytic platform simulation program to calculate vehicle velocities and positions in space-fixed coordinate system
ANGULAR VELOCITY↗
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A 3D Simulation Platform for Decentralized Decision-Making in Advanced Air Mobility
This paper presents a general purpose, plug-and-play simulation platform for the use of future aviation stakeholders, such as urban airspace planners, air vehicle operators, ground operation managers, air traffic controllers and aviation researchers. The presented simulator platform is envisioned to serve as a toolkit to visualize, evaluate, and configure future advanced air mobility (AAM) operations. Highlighting features of this toolkit include a modular architecture that allows multiple smart unmanned aerial systems (UASs) to remotely connect to the simulation server and participate in decentralized decision-making scenario simulations. As an example of the decentralized decision-making scenario, an inter-agent negotiation-based conflict resolution use case is considered in this paper, where the UASs leverage the on-board/on-the-edge artificial intelligence (AI) capability to continually build situational awareness, and use this information to predict future conflicts and resolve them through machine-to-machine negotiation. As such operations are non-existent at scale currently, the presented simulation platform offers a viable and cost-effective alternative for assessing the efficacy of AAM research outcomes and challenges in future shared airspace usage. The simulation platform allows plug-n-play connectivity with AI and non-AI compute modules representing individual UAS’s flight control. Each module can interact with the simulation platform independently to communicate current and desired future states, situational awareness, and conflict resolution utilization costs for inter-agent negotiation. The simulation environment orchestrates realistic operational scenarios with spatiotemporal details, dynamic events, tactical conflict-resolution methods, interfaces for customizing air traffic control parameters, and information exchange uncertainties. In the future, this can serve as a community focused cloud simulation platform, incorporating multi-stakeholder airspace constraints from regulatory, government, city, and local agencies.
Aditya N Das↗
A 3D Simulation Platform for Decentralized Decision-Making in Advanced Air Mobility
This paper presents a general purpose, plug-and-play simulation platform for the use of future aviation stakeholders, such as urban airspace planners, air vehicle operators, ground operation managers, air traffic controllers and aviation researchers. The presented simulator platform is envisioned to serve as a toolkit to visualize, evaluate, and configure future advanced air mobility (AAM) operations. Highlighting features of this toolkit include a modular architecture that allows multiple smart unmanned aerial systems (UASs) to remotely connect to the simulation server and participate in decentralized decision-making scenario simulations. As an example of the decentralized decision-making scenario, an inter-agent negotiation-based conflict resolution use case is considered in this paper, where the UASs leverage the on-board/on-the-edge artificial intelligence (AI) capability to continually build situational awareness, and use this information to predict future conflicts and resolve them through machine-to-machine negotiation. As such operations are non-existent at scale currently, the presented simulation platform offers a viable and cost-effective alternative for assessing the efficacy of AAM research outcomes and challenges in future shared airspace usage. The simulation platform allows plug-n-play connectivity with AI and non-AI compute modules representing individual UAS’s flight control. Each module can interact with the simulation platform independently to communicate current and desired future states, situational awareness, and conflict resolution utilization costs for inter-agent negotiation. The simulation environment orchestrates realistic operational scenarios with spatiotemporal details, dynamic events, tactical conflict-resolution methods, interfaces for customizing air traffic control parameters, and information exchange uncertainties. In the future, this can serve as a community focused cloud simulation platform, incorporating multi-stakeholder airspace constraints from regulatory, government, city, and local agencies.
Aditya Das↗
Initial Study of an Effective Fast-Time Simulation Platform for Unmanned Aircraft System Traffic Management
Small Unmanned Aerial Vehicles (sUAVs), typically 55 lbs and below, are envisioned to play a major role in surveilling critical assets, collecting important information, and delivering goods. Large scale small UAV operations are expected to happen in low altitude airspace in the near future. Many static and dynamic constraints exist in low altitude airspace because of manned aircraft or helicopter activities, various wind conditions, restricted airspace, terrain and man-made buildings, and conflict-avoidance among sUAVs. High sensitivity and high maneuverability are unique characteristics of sUAVs that bring challenges to effective system evaluations and mandate such a simulation platform different from existing simulations that were built for manned air traffic system and large unmanned fixed aircraft. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative focuses on enabling safe and efficient sUAV operations in the future. In order to help define requirements and policies for a safe and efficient UTM system to accommodate a large amount of sUAV operations, it is necessary to develop a fast-time simulation platform that can effectively evaluate requirements, policies, and concepts in a close-to-reality environment. This work analyzed the impacts of some key factors including aforementioned sUAV's characteristics and demonstrated the importance of these factors in a successful UTM fast-time simulation platform.
UTM↗
Initial Study of An Effective Fast-Time Simulation Platform for Unmanned Aircraft System Traffic Management
Small Unmanned Aerial Vehicles (sUAVs), typically 55 lbs and below, are envisioned to play a major role in surveilling critical assets, collecting important information, and delivering goods. Large scale small UAV operations are expected to happen in low altitude airspace in the near future. Many static and dynamic constraints exist in low altitude airspace because of manned aircraft or helicopter activities, various wind conditions, restricted airspace, terrain and man-made buildings, and conflict-avoidance among sUAVs. High sensitivity and high maneuverability are unique characteristics of sUAVs that bring challenges to effective system evaluations and mandate such a simulation platform different from existing simulations that were built for manned air traffic system and large unmanned fixed aircraft. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative focuses on enabling safe and efficient sUAV operations in the future. In order to help define requirements and policies for a safe and efficient UTM system to accommodate a large amount of sUAV operations, it is necessary to develop a fast-time simulation platform that can effectively evaluate requirements, policies, and concepts in a close-to-reality environment. This work analyzed the impacts of some key factors including aforementioned sUAV's characteristics and demonstrated the importance of these factors in a successful UTM fast-time simulation platform.
UTM↗
Flight Simulator Platform Motion and Air Transport Pilot Training
The influence of flight simulator platform motion on pilot training and performance was examined In two studies utilizing a B-727-200 aircraft simulator. The simulator, located at Ames Research Center, Is certified by the FAA for upgrade and transition training in air carrier operations. Subjective ratings and objective performance of experienced B-727 pilots did not reveal any reliable effects of wide variations In platform motion de- sign. Motion platform variations did, however, affect the acquisition of control skill by pilots with no prior heavy aircraft flying experience. The effect was limited to pitch attitude control inputs during the early phase of landing training. Implications for the definition of platform motion requirements in air transport pilot training are discussed.
Lee, Alfred T.↗
Flight simulator platform motion and air transport pilot training
The effect of a flight simulator platform motion on the performance and training of a pilot was evaluated using subjective ratings and objective performance data obtained on experienced B-727 pilots and pilots with no prior heavy aircraft flying experience flying B-727-200 aircraft simulator used by the FAA in the upgrade and transition training for air carrier operations. The results on experienced pilots did not reveal any reliable effects of wide variations in platform motion design. On the other hand, motion variations significantly affected the behavior of pilots without heavy-aircraft experience. The effect was limited to pitch attitude control inputs during the early phase of landing training.
Lee, Alfred T.↗
Full Lunar Surface Visualization and Simulation Platform
This project created a visualization and simulation software platform for the entire lunar surface. Given the polar Artemis landing targets, the challenging terrain, lighting, and associated integrated crew / vehicle risk assessment, a more integrated approach to mission safety certification, potentially impacting equipment design and crew activity is required. This integrated capability will enable Safety and Mission Assurance (SMA), Flight Operations (FOD), and EVA to effectively collaborate to reduce human lunar exploration operational risk utilizing the identical tools to perform mission planning, rehearsal simulations, and providing the Safety Review Panel with integrated risk analysis capability.
information fusion↗
Full Lunar Surface Visualization and Simulation Platform
This paper reports the results of the innovative work completed to facilitate the creation of a lunar surface visualization and simulation platform for the entire surface of the moon as a design, test, training, verification, and mission support environment. The mathematical approach to the visualization of the data sets utilizes fractal mathematics to generate not only the highest possible detail that the Lunar Reconnaissance Orbiter (LRO) data can provide, but it also additionally supplies an estimation of surface reflectivity and lighting based upon location on the lunar surface. This level of detail and accuracy is required to conduct accurate integrated crew / vehicle risk assessment throughout the design-to-disposal life cycle of vehicles and their supporting procedures and mission priorities. This process starts with safety assessments of integrated hardware / software / crew/ and support environments. Unlike the Apollo Program, the Artemis Program is not risk tolerant.
Simulation↗
Array Simulations Platform (ASP) predicts NASA Data Link Module (NDLM) performance
Through a variety of imbedded theoretical and actual antenna patterns, the array simulation platform (ASP) enhanced analysis of the array antenna pattern effects for the KTx (Ku-Band Transmit) service of the NDLM (NASA Data Link Module). The ASP utilizes internally stored models of the NDLM antennas and can develop the overall pattern of antenna arrays through common array calculation techniques. ASP expertly assisted in the diagnosing of element phase shifter errors during KTx testing and was able to accurately predict the overall array pattern from combinations of the four internally held element patterns. This paper provides an overview of the use of the ASP software in the solving of array mis-phasing problems.
Snook, Allen David↗
Development of a Simulation Platform to Evaluate Integration of UAM Traffic into the NAS
A key premise of emerging concepts for Urban Air Mobility (UAM) is that large numbers of these vehicles will be able to seamlessly integrate into the existing National Airspace System (NAS). In this paper, the development of a simulation capability to investigate ways to overcome this technical challenge is presented.
O’Connor, Neil J.↗
Lunar flying platform simulator
Construction and operation of body-motion controlled five-degree-of-freedom simulation of jet-supported lunar flying machine
Hill, P. R.↗
Multi-Platform Avionics Simulator
Multi-Platform Avionics Simulator (MPAvSim) is a software library for development of simulations of avionic hardware. MPAvSim facilitates simulation of interactions between flight software and such avionic peripheral equipment as telecommunication devices, thrusters, pyrotechnic devices, motor controllers, and scientific instruments. MPAvSim focuses on the behavior of avionics as seen by flight software, rather than on performing high-fidelity simulations of dynamics. However, MPAvSim is easily integrable with other programs that do perform such simulations. MPAvSim makes it possible to do real-time partial hardware- in-the-loop simulations. An MPAvSim simulation consists of execution chains (see figure) represented by flow graphs of models, defined here as stateless procedures that do some work. During a simulation, MPAvSim walks the execution chain, running each model in turn. Using MPAvSim, flight software can be run against a spacecraft that is all simulation, all hardware, or part hardware and part simulation. With respect to a specific piece of hardware, either the hardware itself or its simulation can be plugged in without affecting the rest of the system. Thus, flight software can be tested before hardware is available, and as items of hardware become available, they can be substituted for their simulations, with minimal disruption.
Clark, Micah↗
Coding Structures for Seated Row Simulation of an Active Controlled Vibration Isolation and Stabilization System for Astronaut’s Exercise Platform
Simulation for seated aerobic row exercise was a continued task to assist NASA in analyzing a one-dimensional vibration isolation and stabilization system for astronaut’s exercise platform. Feedback delay and signal noise were added to the simulation model. Simulation runs for this study were conducted in two software simulation tools, Trick and MBDyn, software simulation environments developed at the NASA Johnson Space Center. The exciter force in the simulation was calculated from motion capture of an exerciser during a seated aerobic row exercise. The simulation runs include passive control, active control using a Proportional, Integral, Derivative (PID) controller, and active control using a Piecewise Linear Integral Derivative (PWLID) controller. Output parameters include displacements of the exercise platform, the exerciser, and the counterweight; transmitted force to the wall of spacecraft; and actuator force to the platform. The simulation results showed excellent force reduction in the active controlled system compared to the passive controlled system, which resulted in less force reduction.
Simulation↗
Helicopter Flight Simulation Motion Platform Requirements
To determine motion fidelity requirements, a series of piloted simulations was performed. Several key results were found. First, lateral and vertical translational platform cues had significant effects on fidelity. Their presence improved performance and reduced pilot workload. Second, yaw and roll rotational platform cues were not as important as the translational platform cues. In particular, the yaw rotational motion platform cue did not appear at all useful in improving performance or reducing workload. Third, when the lateral translational platform cue was combined with visual yaw rotational cues, pilots believed the platform was rotating when it was not. Thus, simulator systems can be made more efficient by proper combination of platform and visual cues. Fourth, motion fidelity specifications were revised that now provide simulator users with a better prediction of motion fidelity based upon the frequency responses of their motion control laws. Fifth, vertical platform motion affected pilot estimates of steady-state altitude during altitude repositioning. Finally, the combined results led to a general method for configuring helicopter motion systems and for developing simulator tasks that more likely represent actual flight. The overall results can serve as a guide to future simulator designers and to today's operators.
Schroeder, Jeffery Allyn↗
Tele-Supervised Adaptive Ocean Sensor Fleet
The Tele-supervised Adaptive Ocean Sensor Fleet (TAOSF) is a multi-robot science exploration architecture and system that uses a group of robotic boats (the Ocean-Atmosphere Sensor Integration System, or OASIS) to enable in-situ study of ocean surface and subsurface characteristics and the dynamics of such ocean phenomena as coastal pollutants, oil spills, hurricanes, or harmful algal blooms (HABs). The OASIS boats are extended- deployment, autonomous ocean surface vehicles. The TAOSF architecture provides an integrated approach to multi-vehicle coordination and sliding human-vehicle autonomy. One feature of TAOSF is the adaptive re-planning of the activities of the OASIS vessels based on sensor input ( smart sensing) and sensorial coordination among multiple assets. The architecture also incorporates Web-based communications that permit control of the assets over long distances and the sharing of data with remote experts. Autonomous hazard and assistance detection allows the automatic identification of hazards that require human intervention to ensure the safety and integrity of the robotic vehicles, or of science data that require human interpretation and response. Also, the architecture is designed for science analysis of acquired data in order to perform an initial onboard assessment of the presence of specific science signatures of immediate interest. TAOSF integrates and extends five subsystems developed by the participating institutions: Emergent Space Tech - nol ogies, Wallops Flight Facility, NASA s Goddard Space Flight Center (GSFC), Carnegie Mellon University, and Jet Propulsion Laboratory (JPL). The OASIS Autonomous Surface Vehicle (ASV) system, which includes the vessels as well as the land-based control and communications infrastructure developed for them, controls the hardware of each platform (sensors, actuators, etc.), and also provides a low-level waypoint navigation capability. The Multi-Platform Simulation Environment from GSFC is a surrogate for the OASIS ASV system and allows for independent development and testing of higher-level software components. The Platform Communicator acts as a proxy for both actual and simulated platforms. It translates platform-independent messages from the higher control systems to the device-dependent communication protocols. This enables the higher-level control systems to interact identically with heterogeneous actual or simulated platforms.
Lefes, Alberto↗
The use of DSL/90 for the simulation of stabilized platform dynamics
Digital simulation of Saturn guidance system stabilized platform dynamics
STABILIZED PLATFORM↗
Planetary Rover and Space Platform Servicing - Simulation Tools and Network Based Operations
Ground control of space station robotics systems, unmanned space platform servicing, earth-based control of lunar and planetary rover missions, and planetary balloon missions.
space platform servicing telerobotics rover missio↗