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Schedule Analysis Software Saves Time for Project Planners
Since the early 2000s, a resource management team at Marshall Space Flight Center has developed and improved the Schedule Test and Assessment Tool, a software add-on capable of analyzing, summarizing, and finding logic gaps in project schedules. Companies like Lanham, Maryland-based Vantage Systems Inc. use the tool to manage NASA projects, but it has also been released for free to more than 200 US companies, agencies, and other entities.
Challenges and Methods in Testing the Remote Agent Planner
The Remote Agent Experiment (RAX) on the Deep Space 1 (DS1) mission was the first time that an artificially intelligent agent controlled a NASA spacecraft.
An Autonomous Sensor-Based Path-Planner for Planetary Microrovers
With the success of Mars Pathfinder's Sojourner rover, a new era of planetary exploration has opened, with demand for highly capable mobile robots.
V&V of a Spacecraft's Autonomous Planner through Extended Automation
This abstract describes the problem we faced, the solution we employed, and the applicability of our approach in a general V&V setting.
An Overview of the Mars 2020 Perseverance Rover's Enhanced Path-Planner
No abstract provided
APRES Prototype Mission Planner System Demonstration
Activity Planning with Resources for the Exploration of Space (APRES) is a mixed-initiative mission planning system for ground operations. APRES has been designed to support multi-spacecraft missions. The APRES Interface is browser-based and includes a plan editor, a timeline plan display, a temporal constraint editor, display of the state and numeric chronicles, and a violation resolution manager. Automation support is supplied by the APRES Service, which includes components that provide the following capabilities:(1) plan simulation, which determines the state and numeric chronicles (values of the model variables over time) and determines when "processes" are triggered and terminated based on world states in the execution trace, (2) violation detection of constraints and flight rules encoded in the domain model, and of the temporal constraints created by the user, (3) violation resolution suggestions as to how to fix the plan's violations via rescheduling. The user controls when and how to utilize the automation support. Demo video included with paper, runtime 8:54 in color with sound.
Utilizing Reinforcement Learning to Continuously Improve a Primitive-Based Motion Planner.
Abstract not provided.
How to Conduct a Long-Term Planning Study: Guidelines for Power System Planners
This fact sheet provides a high level, step-by-step overview to conduct a power system planning study.
UAS Imaging Path Planner for Heliostat Canting Assessments.
Abstract not provided.
Attract-repel path planner system for collision avoidance
A system for determining a travel direction that avoids objects when a vehicle travels from a current location to a target location is provided. The system determines a travel direction based on an attract-repel model. The system assigns a repel value to the object locations and an attract value. A repel represents a magnitude of a directional repulsive force, and the attract value represents the magnitude of a directional repulsive force. The system calculates an attract-repel field having an attract-repel magnitude and attract-repel direction for the current location based on the repel values and their directions and the attract value and its direction. The system then determines the travel direction for a vehicle to be the direction of the attract-repel field at the current location.
Applications of Autonomous Sensor Task Planner for Intelligence, Surveillance and Reconnaissance.
Abstract not provided.
Applications of Autonomous Sensor Task Planner for Intelligence, Surveillance and Reconnaissance.
Abstract not provided.
Swarm path planner system for vehicles
A system for determining optimal paths without collision through a travel volume for a swarm of vehicles is disclosed. The system determines a travel path for the swarm leader vehicle using a minimal cost path derived from various measures of environmental cost for avoiding objects in traveling from leader location to target location. The system also determines, for each empty neighbor location of each follower vehicle, relational costs for follower vehicle travel relative to leader vehicle travel. The various measures of relational cost seek to maintain a prescribed positional relationship between each follower vehicle and the leader vehicle given the leader vehicle travel path. Based on various measures of environmental and relational cost, the system determines the best travel path for the each follower vehicle relative to the leader vehicle.
Prediction and causal reasoning in planning
Nonlinear planners are often touted as having an efficiency advantage over linear planners. The reason usually given is that nonlinear planners, unlike their linear counterparts, are not forced to make arbitrary commitments to the order in which actions are to be performed. This ability to delay commitment enables nonlinear planners to solve certain problems with far less effort than would be required of linear planners. Here, it is argued that this advantage is bought with a significant reduction in the ability of a nonlinear planner to accurately predict the consequences of actions. Unfortunately, the general problem of predicting the consequences of a partially ordered set of actions is intractable. In gaining the predictive power of linear planners, nonlinear planners sacrifice their efficiency advantage. There are, however, other advantages to nonlinear planning (e.g., the ability to reason about partial orders and incomplete information) that make it well worth the effort needed to extend nonlinear methods. A framework is supplied for causal inference that supports reasoning about partially ordered events and actions whose effects depend upon the context in which they are executed. As an alternative to a complete but potentially exponential-time algorithm, researchers provide a provably sound polynomial-time algorithm for predicting the consequences of partially ordered events.