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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

A Flight Replanning Tool for Terminal Area Urban Air Mobility Operations

In this presentation we describe and evaluate a flight replanning tool, called the trial planner, for terminal area air transport applications. The trial planner employs predefined airspace structures to generate rerouting options between vertiports. In a simplified definition, vertiports are facilities that provide services for managing the take-off and landing of autonomous or manned electric vertical take-off or landing (eVTOL) aircraft. The airspace structures involved in our test cases are arrival routes that consist of predefined entry points to which a transitional path is computed dynamically from the current position of the aircraft. Rerouting options include the transitional path and approach segment along the arrival route; these rerouting options are not vetted for potential conflicts with other operations until a human operator commits to a choice and forwards the flight plan modification for approval from an automated airspace management service. A selected rerouting option is executed by loading a flight plan file to the aircraft ground control station. We conducted a qualitative evaluation of the trial planner using ratings provided by flight crews and air transportation human factors experts with relevant experience from air traffic control. Evaluation criteria included trust in the route recommendations and adequate explanation for the route options. We detail the logic and implementation of the trial planner, as well as report the results of the evaluation of the implementation herein.

Trial planning↗
NASA NTRSDate not supplied

Wildfire monitoring using Unmanned Aerial Vehicles operating under UTM (STEReO)

STEReO (Scalable Traffic Management for Emergency Response Operations) project at NASA Ames is designed to provide UTM (UAS Traffic Management) services to unmanned aerial vehicles (UAVs) used for natural disaster response scenarios like wildfire and hurricanes. This will facilitate the use of unmanned aerial vehicles in regions where UAVs are currently prohibited to fly. In this paper we describe a complete architecture of using UAVs for wild fire monitoring in this STEReO environment. We simulate a complete fire monitoring scenario in an high fidelity simulation environment. The simulation consists of a fire drill in the vicinity of Redding airport, one of the test sites for CAL-FIRE. The autonomous vehicle connects to the STEReO systems and gathers information of other operation in the vicinity. The vehicle then uses on-board path planners and decision making algorithms for fire monitoring and mapping. In this paper the vehicle on-board architecture is described in details and the requirements to fly and interact with the STEReO system is discussed.

UAV UTM↗
NASA NTRS2011

Towards Autonomous Operation of Robonaut 2

The Robonaut 2 (R2) platform, as shown in Figure 1, was designed through a collaboration between NASA and General Motors to be a capable robotic assistant with the dexterity similar to a suited astronaut [1]. An R2 robot was sent to the International Space Station (ISS) in February 2011 and, in doing so, became the first humanoid robot in space. Its capabilities are presently being tested and expanded to increase its usefulness to the crew. Current work on R2 includes the addition of a mobility platform to allow the robot to complete tasks (such as cleaning, maintenance, or simple construction activities) both inside and outside of the ISS. To support these new activities, R2's software architecture is being developed to provide efficient ways of programming robust and autonomous behavior. In particular, a multi-tiered software architecture is proposed that combines principles of low-level feedback control with higher-level planners that accomplish behavioral goals at the task level given the run-time context, user constraints, the health of the system, and so on. The proposed architecture is shown in Figure 2. At the lowest-level, the resource level, there exists the various sensory and motor signals available to the system. The sensory signals for a robot such as R2 include multiple channels of force/torque data, joint or Cartesian positions calculated through the robot's proprioception, and signals derived from objects observable by its cameras.

Badger, Julia M.↗
NASA NTRS2001

RADX34: A Future-X Demonstration of Autonomous Robust Abort Technologies on the X-34

The Charles Stark Draper Laboratory, Inc. has been conducting the RADX-34 Future-X Pathfinder Flight Demonstration experiment under the direction of the NASA Marshall Space Flight Center. The Future-X experiment will use the X-34 to demonstrate autonomous abort technologies needed for the next generation of reusable launch vehicles. This project will involve integration of real-time abort mission planning algorithms with the existing guidance and control to provide abort coverage for the low-Mach flights of the X-34. This paper describes the design and capability of the abort planner three months prior to the conclusion of its development. The mission planner described herein is able to respond to loss of thrust, determine which runways can be reached within the vehicle constraints, and designate an appropriate landing site. This project, when complete, will mark the first step in the development of the Next Generation of Guidance and Control (NGGC) autonomous algorithms. NGGC algorithms will help ensure the second generation of reusable launch vehicles will meet their cost, safety and reliability requirements.

Aron, Edward C.↗
NASA NTRSDate not supplied

NASA Small Spacecraft Technology Program

NASA's Small Spacecraft Technology Program will discuss the objectives, accomplishments, and current status of the Cislunar Autonomous Positioning System Technology Operations and Navigation Experiment (CAPSTONE) mission using the backdrop of CAPSTONE's instance in NASA's Eyes. Tools developed for use by mission planners and small spacecraft designers will be presented by NASA's Small Spacecraft System Virtual Institute.

Elwood F Agasid↗
NASA NTRS2004

Overview of Intelligent Systems and Operations Development

To achieve NASA's ambitious mission objectives for the future, aircraft and spacecraft will need intelligence to take the correct action in a variety of circumstances. Vehicle intelligence can be defined as the ability to "do the right thing" when faced with a complex decision-making situation. It will be necessary to implement integrated autonomous operations and low-level adaptive flight control technologies to direct actions that enhance the safety and success of complex missions despite component failures, degraded performance, operator errors, and environment uncertainty. This paper will describe the array of technologies required to meet these complex objectives. This includes the integration of high-level reasoning and autonomous capabilities with multiple subsystem controllers for robust performance. Future intelligent systems will use models of the system, its environment, and other intelligent agents with which it interacts. They will also require planners, reasoning engines, and adaptive controllers that can recommend or execute commands enabling the system to respond intelligently. The presentation will also address the development of highly dependable software, which is a key component to ensure the reliability of intelligent systems.

Pallix, Joan↗
NASA NTRS2003

Cooperative Exploration of Rough Martian Terrains with the "Scorpion" Legged Robot as an Adjunct to a Rover.

NASA needs autonomous robotic exploration of difficult (rough and/or steep) scientifically interesting Martian terrains. Concepts involving distributed autonomy for cooperative robotic exploration are key to enabling new scientific objectives in robotic missions. We propose to utilize a legged robot as an adjunct scout to a rover for access to difficult - scientifically interesting - terrains (rocky areas, slopes, cliffs). Our final mission scenario involves the Ames rover platform "K9" and Scorpion acting together to explore a steep cliff, with the Scorpion robot rappelling down using the K9 as an anchor as well as mission planner and executive. Cooperation concepts, including wheeled rappelling robots have been proposed before. Now we propose to test the combined advantages of a wheeled vehicle with a legged scout as well as the advantages of merging of high level planning and execution with biologically inspired, behavior based robotics. We propose to use the 8-legged, multifunctional autonomous robot platform Scorpion that is currently capable of: Walking on different terrains (rocks, sand, grass, ...). Perceiving its environment and modifying its behavioral pattern accordingly. These capabilities would be extended to enable the Scorpion to: communicate and cooperate with a partner robot; climb over rocks, rubble piles, and objects with structural features. This will be done in the context of exploration of rough terrains in the neighborhood of the rover, but inaccessible to it, culminating in the added capability of rappelling down a steep cliff for both vertical and horizontal terrain observation.

Colombano, Silvano P.↗
NASA NTRS2002

IDEA: Planning at the Core of Autonomous Reactive Agents

Several successful autonomous systems are separated into technologically diverse functional layers operating at different levels of abstraction. This diversity makes them difficult to implement and validate. In this paper, we present IDEA (Intelligent Distributed Execution Architecture), a unified planning and execution framework. In IDEA a layered system can be implemented as separate agents, one per layer, each representing its interactions with the world in a model. At all levels, the model representation primitives and their semantics is the same. Moreover, each agent relies on a single model, plan database, plan runner and on a variety of planners, both reactive and deliberative. The framework allows the specification of agents that operate, within a guaranteed reaction time and supports flexible specification of reactive vs. deliberative agent behavior. Within the IDEA framework we are working to fully duplicate the functionalities of the DS1 Remote Agent and extend it to domains of higher complexity than autonomous spacecraft control.

Muscettola, Nicola↗
NASA NTRS2010

Water Detection Based on Sky Reflections

This software has been designed to detect water bodies that are out in the open on cross-country terrain at mid- to far-range (approximately 20 100 meters), using imagery acquired from a stereo pair of color cameras mounted on a terrestrial, unmanned ground vehicle (UGV). Non-traversable water bodies, such as large puddles, ponds, and lakes, are indirectly detected by detecting reflections of the sky below the horizon in color imagery. The appearance of water bodies in color imagery largely depends on the ratio of light reflected off the water surface to the light coming out of the water body. When a water body is far away, the angle of incidence is large, and the light reflected off the water surface dominates. We have exploited this behavior to detect water bodies out in the open at mid- to far-range. When a water body is detected at far range, a UGV s path planner can begin to look for alternate routes to the goal position sooner, rather than later. As a result, detecting water hazards at far range generally reduces the time required to reach a goal position during autonomous navigation. This software implements a new water detector based on sky reflections that geometrically locates the exact pixel in the sky that is reflecting on a candidate water pixel on the ground, and predicts if the ground pixel is water based on color similarity and local terrain features

Rankin, Arturo L.↗
NASA NTRS2020

Hybrid Electric MC-12 Ground Testing Plan Chapter

In the commercial aviation world, hybrid/electric propulsion is a promising technology for fuel, emissions, and noise reduction in support of the challenging goals established by 2050 EU Flightpath/SRIA, NASA ARMD Strategic Implementation Plan, and the US Air Force ATTAM programs. Ongoing results indicate operational benefits are possible in those three technical areas. Considering military applications, hybrid/electric propulsion may yield further significant improvements by enabling new, unorthodox mission capabilities. Potential benefits are expected in the areas of vehicle signature reduction (lower noise, lower exhaust signature), usage in enhanced flight environments, minimized human-in-the-loop workload by offering a platform compatible with future goals of autonomous operations facilitation, maintenance cost reductions, and performance burst/dash energy. Additional synergies are likely when used in conjunction with energy weapons. Some potential areas of mutual interest could include, dusty operations capability, remote supply capability, extended surveillance, dispatch able power, fuel-flexible vehicles, and autonomous rescue equipment. Many of the key technologies that have been demonstrated in ground operation must now be qualified for altitude conditions. We expect to have many flight qualified powertrain systems over the next decade for military planners to choose from for new future battlefield capability. This requires new flight-weight and flight-efficient powertrain components, fault tolerant power management, and electromagnetic interference mitigation technologies. Moreover, initial studies indicate some combination of ambient and cryogenic thermal management and relatively high bus voltages when compared to state of practice will be required to achieve a net system benefit. Developing all these powertrain technologies within a realistic aircraft architectural geometry and under realistic operational conditions requires a unique electric aircraft test bed. The MC-12 surveillance aircraft is an ideal military aircraft for demonstrating some of the benefits of incorporating hybrid electric propulsion. This report details an approach for full-scale ground-based altitude testing the hybrid electric MC-12 powertrain through a full flight profile.

military electric aircraft propulsion↗
NASA NTRS2003

On-board autonomous attitude maneuver planning for planetary spacecraft using genetic algorithms

A key enabling technology that leads to greater spacecraft autonomy is the capability to autonomously and optimally slew the spacecraft from and to different attitudes while operating under a number of celestial and dynamic constraints. The task of finding an attitude trajectory that meets all the constraints is a formidable one, in particular for orbiting or fly-by spacecraft where the constraints and initial and final conditions are of time-varying nature. This paper presents an approach for attitude path planning that makes full use of a priori constraint knowledge and is computationally tractable enough to be executed on-board a spacecraft. The approach is based on incorporating the constraints into a cost function and using a Genetic Algorithm to iteratively search for and optimize the solution. This results in a directed random search that explores a large part of the solution space while maintaining the knowledge of good solutions from iteration to iteration. A solution obtained this way may be used 'as is' or as an initial solution to initialize additional deterministic optimization algorithms. A number of example simulations are presented including the case examples of a generic Europa Orbiter spacecraft in cruise as well as in orbit around Europa. The search times are typically on the order of minutes, thus demonstrating the viability of the presented approach. The results are applicable to all future deep space missions where greater spacecraft autonomy is required. In addition, onboard autonomous attitude planning greatly facilitates navigation and science observation planning, benefiting thus all missions to planet Earth as well.

genetic algorithm↗
NASA NTRS2020

Navigation on the Line: Traversability Analysis and Path Planning for Extreme-Terrain Rappelling Rovers

Many areas of scientific interest in planetaryexploration, such as lunar pits, icy-moon crevasses, and Martiancraters, are inaccessible to current wheeled rovers. Rappellingrovers can safely traverse these steep surfaces, but requiretechniques to navigate their complex terrain. This dynamicnavigation is inherently time-critical and communication constraints(e.g. delays and small communication windows) willrequire planetary systems to have some autonomy.Autonomous navigation for Martian rovers is well studiedon moderately sloped and locally planar surfaces, but thesemethods do not readily transfer to tethered systems in nonplanar3D environments. Rappelling rovers in these situationshave additional challenges, including terrain-tether interactionand its effects on rover stability, path planning and control.This paper presents novel traversability analysis and pathplanning algorithms for rappelling rovers operating on steepterrains that account for terrain-tether interaction and theunique stability and reachability constraints of a rapellingsystem. The system is evaluated with a series of simulations andan analogue mission. In simulation, the planner was shown toreliably find safe paths down a 55 degree slope when a stabletether-terrain configuration exists and never recommended anunsafe path when one did not. In a planetary analogue mission,elements of the system were used to autonomously navigateAxel, a JPL rappelling rover, down a 30 degree slope with95% autonomy by distance travelled over 46 meters.

Nesnas, Issa↗
NASA NTRS2017

Multi-Modal Active Perception for Autonomously Selecting Landing Sites on Icy Moons

Selecting suitable landing sites is fundamental to achieving many mission objectives in planetary robotic lander missions. However, due to sensing limitations, landing sites which are both safe and scientifically valuable often cannot be determined reliably from orbit, particularly, in icy moon missions where orbital sensing data is noisy and incomplete. This paper presents an active perception approach to Entry Descent and Landing (EDL) which enables the lander to autonomously plan informative descent trajectories, acquire high quality sensing data during descent and exploit this additional information to select higher utility landing sites. Our approach consists of two components: probabilistic modeling of landing site features and approximate trajectory planning using a sampling based planner. The proposed framework allows the lander to plan long horizons paths and remain robust to noisy data. Results in simulated environments show large performance improvements over alternative approaches and show promise that our approach has strong potential to improve science return of not only icy moon missions but EDL systems in general.

autonomy↗
NASA NTRS2005

Lessons Learned in the Livingstone 2 on Earth Observing One Flight Experiment

The Livingstone 2 (L2) model-based diagnosis software is a reusable diagnostic tool for monitoring complex systems. In 2004, L2 was integrated with the JPL Autonomous Sciencecraft Experiment (ASE) and deployed on-board Goddard's Earth Observing One (EO-1) remote sensing satellite, to monitor and diagnose the EO-1 space science instruments and imaging sequence. This paper reports on lessons learned from this flight experiment. The goals for this experiment, including validation of minimum success criteria and of a series of diagnostic scenarios, have all been successfully net. Long-term operations in space are on-going, as a test of the maturity of the system, with L2 performance remaining flawless. L2 has demonstrated the ability to track the state of the system during nominal operations, detect simulated abnormalities in operations and isolate failures to their root cause fault. Specific advances demonstrated include diagnosis of ambiguity groups rather than a single fault candidate; hypothesis revision given new sensor evidence about the state of the system; and the capability to check for faults in a dynamic system without having to wait until the system is quiescent. The major benefits of this advanced health management technology are to increase mission duration and reliability through intelligent fault protection, and robust autonomous operations with reduced dependency on supervisory operations from Earth. The work-load for operators will be reduced by telemetry of processed state-of-health information rather than raw data. The long-term vision is that of making diagnosis available to the onboard planner or executive, allowing autonomy software to re-plan in order to work around known component failures. For a system that is expected to evolve substantially over its lifetime, as for the International Space Station, the model-based approach has definite advantages over rule-based expert systems and limit-checking fault protection systems, as these do not scale well. The model-based approach facilitates reuse of the L2 diagnostic software; only the model of the system to be diagnosed and telemetry monitoring software has to be rebuilt for a new system or expanded for a growing system. The hierarchical L2 model supports modularity and expendability, and as such is suitable solution for integrated system health management as envisioned for systems-of-systems.

Hayden, Sandra C.↗
NASA NTRS2022

Towards Finding Energy Efficient Paths for Hybrid Airships in the Atmosphere of Venus

This paper presents a solution to the motion planning problem for an autonomous airship under superrotation winds of the Venusian atmosphere. The airship uses both buoyancy and aerodynamic lift to control its altitude. In addition, solar panels distributed over the aircraft provide energy to the propellers and allow for battery recharging. Our approach uses a sampling-based planner that relies on Dubins’ Airplane paths deformed under the influence of the winds to create a tree of kinematically feasible trajectories. We use the battery state to prune energetically unfeasible trajectories and we propose a cost function that accounts for the energy expenditure of the propulsive system and that considers battery charging by using the Economics notion of opportunity cost. The method is illustrated through a series of simulations that show how the vehicle takes longer and high-altitude paths to minimize the use of energy and favor battery recharge. Our results also show that naive trajectories are not feasible in terms of energy, justifying the need for more efficient solutions.

Bernardo Martinez R. Jr↗
NASA NTRS1987

Mission planning for autonomous systems

Planning is a necessary task for intelligent, adaptive systems operating independently of human controllers. A mission planning system that performs task planning by decomposing a high-level mission objective into subtasks and synthesizing a plan for those tasks at varying levels of abstraction is discussed. Researchers use a blackboard architecture to partition the search space and direct the focus of attention of the planner. Using advanced planning techniques, they can control plan synthesis for the complex planning tasks involved in mission planning.

Pearson, G.↗
NASA NTRS2008

Automated and Adaptive Mission Planning for Orbital Express

The Orbital Express space mission was a Defense Advanced Research Projects Agency (DARPA) lead demonstration of on-orbit satellite servicing scenarios, autonomous rendezvous, fluid transfers of hydrazine propellant, and robotic arm transfers of Orbital Replacement Unit (ORU) components. Boeing's Autonomous Space Transport Robotic Operations (ASTRO) vehicle provided the servicing to the Ball Aerospace's Next Generation Serviceable Satellite (NextSat) client. For communication opportunities, operations used the high-bandwidth ground-based Air Force Satellite Control Network (AFSCN) along with the relatively low-bandwidth GEO-Synchronous space-borne Tracking and Data Relay Satellite System (TDRSS) network. Mission operations were conducted out of the RDT&E Support Complex (RSC) at the Kirtland Air Force Base in New Mexico. All mission objectives were met successfully: The first of several autonomous rendezvous was demonstrated on May 5, 2007; autonomous free-flyer capture was demonstrated on June 22, 2007; the fluid and ORU transfers throughout the mission were successful. Planning operations for the mission were conducted by a team of personnel including Flight Directors, who were responsible for verifying the steps and contacts within the procedures, the Rendezvous Planners who would compute the locations and visibilities of the spacecraft, the Scenario Resource Planners (SRPs), who were concerned with assignment of communications windows, monitoring of resources, and sending commands to the ASTRO spacecraft, and the Mission planners who would interface with the real-time operations environment, process planning products and coordinate activities with the SRP. The SRP position was staffed by JPL personnel who used the Automated Scheduling and Planning ENvironment (ASPEN) to model and enforce mission and satellite constraints. The lifecycle of a plan began three weeks outside its execution on-board. During the planning timeframe, many aspects could change the plan, causing the need for re-planning. These variable factors, ranging from shifting contact times to ground-station closures and required maintenance times, are discussed along with the flexibility of the ASPEN tool to accommodate changes to procedures and the daily or long-range plan, which contributed to the success of the mission. This paper will present an introduction to ASPEN, a more in-depth discussion on its use on the Orbital Express mission, and other relative work. A description of ground operations after the SRP deliveries were made is included, and we briefly discuss lessons learned from the planning perspective and future work.

scheduling↗
NASA NTRS1989

Telerobotic Architecture for an on-Orbit Servicer

An on-orbit servicer system has unique functional and human factors requirements. The servicing, whether it be teleoperation task, a supervised control task or an autonomous robotic task, the man-machine interface function is likely to be a bottleneck to the operation of the whole system. The man-machine interface system for a space servicer, namely the operator control station, includes several subsystems with a hierarchical architecture. Those subsystems include a reasoning and planning subsystem (also known as the artificial intelligence planner), a run-time control subsystem, a manipulator control and mechanization subsystem, and a sensing and perception subsystem. Indicative of these potentials, certain generic tasks, suggestive of space assembly, maintenance and repair, were performed in a testbed environment. Through performance in several modes: direct teleoperation, shared control, traded control, and robotic operation, the benefits of the individual technology contributions to the operation were quantized and recommendations for use in telerobotic systems were established.

Operator Control Station↗