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Results for “Trajectory Replanning”

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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23 records · Page 2

Multiagent Flight Control in Dynamic Environments with Cooperative Coevolutionary Algorithms

Dynamic flight environments in which objectives and environmental features change with respect to time pose a difficult problem with regards to planning optimal flight paths. Path planning methods are typically computationally expensive, and are often difficult to implement in real time if system objectives are changed. This computational problem is compounded when multiple agents are present in the system, as the state and action space grows exponentially. In this work, we use cooperative coevolutionary algorithms in order to develop policies which control agent motion in a dynamic multiagent unmanned aerial system environment such that goals and perceptions change, while ensuring safety constraints are not violated. Rather than replanning new paths when the environment changes, we develop a policy which can map the new environmental features to a trajectory for the agent while ensuring safe and reliable operation, while providing 92% of the theoretically optimal performance

Experimentation↗

Simple Pattern Traffic Generation for Automated Flight Research in Non-Towered Traffic Patterns

Research efforts into autonomous air traffic will necessitate tools for testing algorithm capabilities. Testing will require flexible methods for creating large quantities of artificial data to verify the safety of automated systems. The traffic generation method in this work was developed to test traffic prediction and replanning algorithms for an autonomous vehicle attempting to land at a non-towered airport. The traffic generation method produces airport approach trajectories supporting a wide range of pattern entry types and typical pattern modification maneuvers for multiple aircraft types with varying performance capabilities. For each aircraft there are options for choosing the approach type, modifying how the approach is flown, and imposing scenario-driven temporal constraints, such as spacing between pairs of aircraft. The tool uses simplified aircraft dynamics to produce position and velocity profiles for traffic vehicles. Additionally, the tool supports standalone simulation tests or batch/bulk testing efforts, multiple output data options, and facilitates post-processing analysis.

Autonomous Vehicle↗

Vehicle management and mission planning in support of shuttle operations.

An operational approach to shuttle mission planning during high flight frequency years (20 or more flights per year) is described wherein diverse mission planning functions interface via an interactive computer system and common data base. The Vehicle Management and Mission Planning System (VMMPS) is proposed as a means of helping to accomplish the mission planning function. The VMMPS will link together into an interactive system the major mission planning areas such as trajectory, crew, vehicle performance, and launch operations. A common data base will be an integral part of the system and the concept of standard mission types and phases will be used to minimize mission to mission uniqueness. The use of this system will eliminate much redundancy and replanning, shorten interface times between functions, and provide a means to evaluate unplanned events and modify schedules.

Pruett, W. R.↗

The Strategy for the Second Phase of Aerobraking Mars Global Surveyor

On February 19, 1999, the Mars Global Surveyor (MGS) spacecraft was able to propulsively establish its mapping orbit. This event followed the completion of the second phase of aerobraking for the MGS spacecraft on February 4, 1999. For the first time, a spacecraft at Mars had successfully employed aerobraking methods in order to reach its desired pre-launch mapping orbit. This was accomplished despite a damaged spacecraft solar array. The MGS spacecraft was launched on November 7, 1996, and after a ten month interplanetary transit was inserted into a highly elliptical capture orbit at Mars on September 12, 1997. Unlike other interplanetary missions, the MGS spacecraft was launched with a planned mission delta-V ((Delta)V) deficit of nearly 1250 m/s. To overcome this AV deficit, aerobraking techniques were employed. However, damage discovered to one of the spacecraft's two solar arrays after launch forced major revisions to the original aerobraking planning of the MGS mission. In order to avoid a complete structural failure of the array, peak dynamic pressure levels for the spacecraft were established at a major spacecraft health review in November 1997. These peak dynamic pressure levels were roughly one-third of the original mission design values. Incorporating the new dynamic pressure limitations into mission replanning efforts resulted in an 'extended' orbit insertion phase for the mission. This 'extended' orbit insertion phase was characterized by two distinct periods of aerobraking separated by an aerobraking hiatus that would last for several months in an intermediate orbit called the "Science Phasing Orbit" (SPO). This paper describes and focuses on the strategy for the second phase of aerobraking for the MGS mission called "Aerobraking Phase 2." This description will include the baseline aerobraking flight profile, the trajectory control methodology, as well as the key trajectory metrics that were monitored in order to successfully "guide' the spacecraft to its desired mapping orbit. Additionally, the actual aerobraking progress is contrasted to the planned aerobraking flight profile. (A separate paper will describe the navigation aspects of MGS aerobraking in detail.) Key to the success of the MGS mission is the delivery of the spacecraft to its final mapping orbit and the synergy the instrument complement provides to its scientific investigators when science data is returned from that orbit. The MGS mapping orbit is characterized as a low altitude, near-circular, near-polar orbit that is Sun-synchronous with the descending equatorial crossing at 2:00 AM local mean solar time (LMST).

Johnston, M. D.↗

Methods for Determining the Level of Autonomy to Design into a Human Spaceflight Vehicle: A Function Specific Approach

The next-generation human spaceflight vehicle is in a unique position to realize the benefits of more than thirty years of technological advancements since the Space Shuttle was designed. Computer enhancements, the emergence of highly reliable decision-making algorithms, and an emphasis on efficiency make an increased use of autonomous systems highly likely. NASA is in a position to take advantage of these advances and apply them to the human spaceflight environment. One of the key paradigm shifts will be the shift, where appropriate, of monitoring, option development, decision-making, and execution responsibility from humans to an Autonomous Flight Management (AFM) system. As an effort to reduce risk for development of an AFM system, NASA engineers are developing a prototype to prove the utility of previously untested autonomy concepts. This prototype, called SMART (Spacecraft Mission Assessment and Replanning Tool), is a functionally decomposed flight management system with an appropriate level of autonomy for each of its functions. As the development of SMART began, the most important and most often asked question was, How autonomous should an AFM system be? A thorough study of the literature through 2002 surrounding autonomous systems has not yielded a standard method for designing a level of autonomy into either a crewed vehicle or an uncrewed vehicle. The current focus in the literature on defining autonomy is centered on developing IQ tests for built systems. The literature that was analyzed assumes that the goal of all systems is to strive for complete autonomy from human intervention, rather than identifying how autonomous each function within the system should have been. In contrast, the SMART team developed a method for determining the appropriate level of autonomy to be designed into each function within a system. This paper summarizes the development of the Level of Autonomy Assessment Tool and its application to the SMART project.

AFM (AUTONOMOUS FLIGHT MANAGEMENT)↗