Rover missions typically involve visiting a set of predetermined waypoints to perform science functions, such as sample collection. Given the communication delay between Earth and the rover, and the possible occurrence of faults, an autonomous decision making system is essential to ensure that the rover maximizes the scientific operations performed without damaging itself further or stalling. This paper presents a modular software architecture for autonomous decision making for rover operations that uses diagnostic and prognostic information to influence mission planning and decision making to maximize the completion of mission objectives. The decision making system consists of separate modules that perform the functions of control, diagnosis, prognosis, and decision making.We demonstrate our implementation of this architecture on a simulated rover testbed.

Every one of the future exploration architectures being considered by NASA have, at their core, the need to rendezvous and dock with other vehicles or bodies.  Future manned vehicles need to be able to do so with both cooperative and uncooperative vehicles and objects.  To this end, the sensors being considered are all optical-based.  In fact, passive sensors, such as IR cameras and visual cameras, are at the heart of any exploration architecture.  There is a need for the onboard systems to be able to use the images provided by these sensors to rendezvous and dock/capture these objects.  Therefore, this project will develop this capability to operate around a variety of objects, without a priori knowledge of their geometry.  In particular, a technology called ‘optical flow’ or ‘visual odometry’ (VO), will be harnessed to develop a robust on-board capability using passive sensors; of course, if active sensors are available, they will be used as well. In fact, we will also apply this technique to navigating around a cratered object (such as an asteroid). This project will enhance the Agency’s ability to operate at distant locations, without the need for ground intervention.

To date, all of the on-board navigation development performed has focused on either Low Earth Orbit (LEO) or Low Lunar Orbit (LLO).  We seek to advance deep-space navigation technology by focusing this Internal Research and Development (IRAD) upon rendezvous and navigation in a weak gravity environment, either at Lagrangian point 2 (L2) or around an asteroid.  Of course, this will apply to any destinations that have a strong gravity field as well.  As well, the technology developed in this Internal Research and Development will apply to rendezvousing with vehicles such as ISS.  We choose to focus our IRAD effort on the navigation algorithms and software for the ARCM DRO Mission, thus broadening our scope, maintaining our cutting-edge capability, and advancing US manned space exploration.  The goal is to be flexible enough to meet the needs of the NASA vision, as it applies to any destination the Agency chooses to embark upon.

 

GOES는 NOAA/NASA가 공동협력하여 개발 운영한 기상위성으로 NASA에서 연구개발 및 운영을 책임지고 서경 135에서 기상업무를 수행하였습니다. 2003년에 일본 기상위성 GMS-5 대체로 동경 155에서 기상업무를 수행하였습니다.

Originally designed to carry the towering Saturn V moon rocket from the Vehicle Assembly Building to the seaside launch site, the enormous transporters now carry the space shuttles to the launch pads for liftoff. Polygons: 146050 Vertices: 141658

Originally designed to carry the towering Saturn V moon rocket from the Vehicle Assembly Building to the seaside launch site, the enormous transporters now carry the space shuttles to the launch pads for liftoff. Polygons: 146050 Vertices: 141658

Future NASA plans call for long-duration deep space missions with human crews. Because of light-time delay and other considerations, increased autonomy will be needed. This will necessitate integration of tools in such areas as anomaly detection, diagnosis, planning, and execution. In this paper we investigate an approach that integrates planning and execution by embedding planner-derived temporal constraints in an execution procedure. To avoid the need for propagation, we convert the temporal constraints to dispatchable form. We handle some uncertainty in the durations without it affecting the execution; larger variations may cause activities to be skipped.

Model of the Mars Global Surveyor spacecraft. Polygons: 19314 Vertices: 10250

Model of the Mars Global Surveyor spacecraft. Polygons: 19314 Vertices: 10236

Model of the Mars Global Surveyor spacecraft. Polygons: 19314 Vertices: 10250