Engineering topics

Graf, Jodi

Publications and source records attributed to Graf, Jodi.

6 records

NASA NTRS2017

System and Method for Traversing Pipes

A system and method is provided for traversing inside one or more pipes. In an embodiment, a fluid is injected into the one or more pipes thereby promoting a fluid flow. An inspection device is deployed into the one or more pipes at least partially filled with a flowing fluid. The inspection device comprises a housing wherein the housing is designed to exploit the hydrokinetic effects associated with a fluid flow in one or more pipes as well as maneuver past a variety of pipe configurations. The inspection device may contain one or more sensors capable of performing a variety of inspection tasks.

Azimi, Shaun↗

NASA NTRS2012

Tele-Operated Lunar Rover Navigation Using Lidar

Near real-time tele-operated driving on the lunar surface remains constrained by bandwidth and signal latency despite the Moon s relative proximity. As part of our work within NASA s Human-Robotic Systems Project (HRS), we have developed a stand-alone modular LIDAR based safeguarded tele-operation system of hardware, middleware, navigation software and user interface. The system has been installed and tested on two distinct NASA rovers-JSC s Centaur2 lunar rover prototype and ARC s KRex research rover- and tested over several kilometers of tele-operated driving at average sustained speeds of 0.15 - 0.25 m/s around rocks, slopes and simulated lunar craters using a deliberately constrained telemetry link. The navigation system builds onboard terrain and hazard maps, returning highest priority sections to the off-board operator as permitted by bandwidth availability. It also analyzes hazard maps onboard and can stop the vehicle prior to contacting hazards. It is robust to severe pose errors and uses a novel scan alignment algorithm to compensate for attitude and elevation errors.

Pedersen, Liam↗

NASA NTRS2011

The Traverse Planning Process for the Drats 2010 Analog Field Simulations

Traverse planning concentrates on optimizing the science return within the overall objectives of planetary surface missions or their analog field simulations. Such simulations were conducted in the San Francisco Volcanic Field, northern Arizona, from Aug. 26 to Sept 17, 2010 and involved some 200 individuals in the field, with some 40 geoscientists composing the science team. The purpose of these Desert Research and Technology Studies (DRATS) is to exercise and evaluate developmental hardware, software and operational concepts in a mission-like, fully-integrated, setting under the direction of an onsite Mobile Mission Control Center(MMCC). DRATS 2010 focused on the simultaneous operation of 2 rovers, a historic first. Each vehicle was manned by an astronaut-commander and an experienced field geologist. Having 2 rovers and crews in the field mandated substantially more complex science and mission control operations compared to the single rover DRATS tests of 2008 and 2009, or the Apollo lunar missions. For instance, the science support function was distributed over 2 "back rooms", one for each rover, with both "tactical" teams operating independently and simultaneously during the actual traverses. Synthesis and integration of the daily findings and forward planning for the next day(s) was accomplished overnight by yet another "strategic" science team.

Horz, Friedrich↗

NASA NTRS2011

Crew Field Notes: A New Tool for Planetary Surface Exploration

The Desert Research and Technology Studies (DRATS) field tests of 2010 focused on the simultaneous operation of two rovers, a historical first. The complexity and data volume of two rovers operating simultaneously presented significant operational challenges for the on-site Mission Control Center, including the real time science support function. The latter was split into two "tactical" back rooms, one for each rover, that supported the real time traverse activities; in addition, a "strategic" science team convened overnight to synthesize the day's findings, and to conduct the strategic forward planning of the next day or days as detailed in [1, 2]. Current DRATS simulations and operations differ dramatically from those of Apollo, including the most evolved Apollo 15-17 missions, due to the advent of digital technologies. Modern digital still and video cameras, combined with the capability for real time transmission of large volumes of data, including multiple video streams, offer the prospect for the ground based science support room(s) in Mission Control to witness all crew activities in unprecedented detail and in real time. It was not uncommon during DRATS 2010 that each tactical science back room simultaneously received some 4-6 video streams from cameras mounted on the rover or the crews' backpacks. Some of the rover cameras are controllable PZT (pan, zoom, tilt) devices that can be operated by the crews (during extensive drives) or remotely by the back room (during EVAs). Typically, a dedicated "expert" and professional geologist in the tactical back room(s) controls, monitors and analyses a single video stream and provides the findings to the team, commonly supported by screen-saved images. It seems obvious, that the real time comprehension and synthesis of the verbal descriptions, extensive imagery, and other information (e.g. navigation data; time lines etc) flowing into the science support room(s) constitute a fundamental challenge to future mission operations: how can one analyze, comprehend and synthesize -in real time- the enormous data volume coming to the ground? Real time understanding of all data is needed for constructive interaction with the surface crews, and it becomes critical for the strategic forward planning process.

Horz, Friedrich↗

NASA NTRS2006

Humanoid Mobile Manipulation Using Controller Refinement

An important class of mobile manipulation problems are move-to-grasp problems where a mobile robot must navigate to and pick up an object. One of the distinguishing features of this class of tasks is its coarse-to-fine structure. Near the beginning of the task, the robot can only sense the target object coarsely or indirectly and make gross motion toward the object. However, after the robot has located and approached the object, the robot must finely control its grasping contacts using precise visual and haptic feedback. In this paper, it is proposed that move-to-grasp problems are naturally solved by a sequence of controllers that iteratively refines what ultimately becomes the final solution. This paper introduces the notion of a refining sequence of controllers and characterizes this type of solution. The approach is demonstrated in a move-to-grasp task where Robonaut, the NASA/JSC dexterous humanoid, is mounted on a mobile base and navigates to and picks up a geological sample box. In a series of tests, it is shown that a refining sequence of controllers decreases variance in robot configuration relative to the sample box until a successful grasp has been achieved.

Platt, Robert↗

NASA NTRS2006

Humanoid Mobile Manipulation Using Controller Refinement

An important class of mobile manipulation problems are move-to-grasp problems where a mobile robot must navigate to and pick up an object. One of the distinguishing features of this class of tasks is its coarse-to-fine structure. Near the beginning of the task, the robot can only sense the target object coarsely or indirectly and make gross motion toward the object. However, after the robot has located and approached the object, the robot must finely control its grasping contacts using precise visual and haptic feedback. This paper proposes that move-to-grasp problems are naturally solved by a sequence of controllers that iteratively refines what ultimately becomes the final solution. This paper introduces the notion of a refining sequence of controllers and characterizes this type of solution. The approach is demonstrated in a move-to-grasp task where Robonaut, the NASA/JSC dexterous humanoid, is mounted on a mobile base and navigates to and picks up a geological sample box. In a series of tests, it is shown that a refining sequence of controllers decreases variance in robot configuration relative to the sample box until a successful grasp has been achieved.

Platt, Robert↗