Engineering topics
Publications and source records attributed to Thomas, Hans.
The Personal Satellite Assistant (PSA) is a softball-sized flying robot designed to operate autonomously onboard manned spacecraft in pressurized micro-gravity environments. We describe how the Brahms multi-agent modeling and simulation environment in conjunction with a KAoS agent teamwork approach can be used to support human-centered design for the PSA.
Rovers are a vital component of NASA's strategy for manned and unmanned exploration of space. For the past five years, the Intelligent Mechanisms Group at the NASA Ames Research Center has conducted a vigorous program of field testing of rovers from both technology and science team productivity perspective. In this talk, I will give an overview of the the last two years of the test program, focusing on tests conducted in the Painted Desert of Arizona, the Atacama desert in Chile, and on IMG participation in the Mars Pathfinder mission. An overview of autonomy, manipulation, and user interface technologies developed in response to these missions will be presented, and lesson's learned in these missions and their impact on future flight missions will be presented. I will close with some perspectives on how the testing program has affected current rover systems.
A geologist characterizing a field site typically wanders from one interesting geologic feature to another. Performing geology remotely with a mobile robot, we have observed the same behavior: geologists see a visually-interesting feature and wish to approach it for closer inspection. To date, navigating a mobile robot to a visually-interesting feature has been accomplished by driving to a location close to the feature. This introduces two problems: assigning a location to the feature and navigating the rover to that location. In practice, solutions to both of these problems are susceptable to positional error. Fundamentally, we can see where we want the robot to go, but it is difficult to precisely quantify where either the target or the robot are located. The development of vision-based control of robot manipulators suggests an alternative approach for mobile robot explorers. We have developed a vision-based control system that enables the Marsokhod rover to drive to within sampling distance of visually-designated rock or natural feature. We will describe this system and our initial results using it during a field experiment in the Painted Desert of Arizona.
The state-of-the-art in computing technology is rapidly attaining the performance necessary to implement many early vision algorithms at real-time rates. This new capability is helping to accelerate progress in vision research by improving our ability to evaluate the performance of algorithms in dynamic environments. In particular, we are becoming much more aware of the relative stability of various visual measurements in the presence of camera motion and system noise. This new processing speed is also allowing us to raise our sights toward accomplishing much higher-level processing tasks, such as figure-ground separation and active object tracking, in real-time. This paper describes a methodology for using early visual measurements to accomplish higher-level tasks; it then presents an overview of the high-speed accelerators developed at Teleos to support early visual measurements. The final section describes the successful deployment of a real-time vision system to provide visual perception for the Extravehicular Activity Helper/Retriever robotic system in tests aboard NASA's KC135 reduced gravity aircraft.
A hierarchy of planning strategies is proposed and explained for a walking robot called the Ambler. The hierarchy decomposes planning into levels of trajectory, gait, and footfall. An abstraction of feasible traversability allows the Ambler's trajectory planner to identify acceptable trajectories by finding paths that guarantee footfalls without specifying exactly which footfalls. Leg and body moves that achieve this trajectory can be generated by the Ambler's gait planner, which incorporates pattern constraints and measures of utility to search for the best next move. By combining constraints from the quality and details of the terrain, the Ambler's footfall planner can select footfalls that insure stability and remain within the tolerances of the gait.