Engineering topics

Angelova, Anelia

Publications and source records attributed to Angelova, Anelia.

7 records

NASA NTRS2008

Terrain-Adaptive Navigation Architecture

A navigation system designed for a Mars rover has been designed to deal with rough terrain and/or potential slip when evaluating and executing paths. The system also can be used for any off-road, autonomous vehicles. The system enables vehicles to autonomously navigate different terrain challenges including dry river channel systems, putative shorelines, and gullies emanating from canyon walls. Several of the technologies within this innovation increase the navigation system s capabilities compared to earlier rover navigation algorithms.

Helmick, Daniel M.↗

NASA NTRS2007

Terrain Adaptive Navigation for Mars Rovers

A navigation system for Mars rovers in very rough terrain has been designed, implemented, and tested on a research rover in Mars analog terrain. This navigation system consists of several technologies that are integrated to increase the capabilities compared to current rover navigation algorithms. These technologies include: goodness maps and terrain triage, terrain classification, remote slip prediction, path planning, high-fidelity traversability analysis (HFTA), and slip-compensated path following. The focus of this paper is not on the component technologies, but rather on the integration of these components. Results from the onboard integration of several of the key technologies described here are shown. Additionally, the results from independent demonstrations of several of these technologies are shown. Future work will include the demonstration of the entire integrated system described here.

traversabiity analysis↗

NASA NTRS2007

Learning and Prediction of Slip from Visual Information

This paper presents an approach for slip prediction from a distance for wheeled ground robots using visual information as input. Large amounts of slippage which can occur on certain surfaces, such as sandy slopes, will negatively affect rover mobility. Therefore, obtaining information about slip before entering such terrain can be very useful for better planning and avoiding these areas. To address this problem, terrain appearance and geometry information about map cells are correlated to the slip measured by the rover while traversing each cell. This relationship is learned from previous experience, so slip can be predicted remotely from visual information only. The proposed method consists of terrain type recognition and nonlinear regression modeling. The method has been implemented and tested offline on several off-road terrains including: soil, sand, gravel, and woodchips. The final slip prediction error is about 20%. The system is intended for improved navigation on steep slopes and rough terrain for Mars rovers.

rover↗

NASA NTRS2006

Learning to predict slip for ground robots

In this paper we predict the amount of slip an exploration rover would experience using stereo imagery by learning from previous examples of traversing similar terrain. To do that, the information of terrain appearance and geometry regarding some location is correlated to the slip measured by the rover while this location is being traversed. This relationship is learned from previous experience, so slip can be predicted later at a distance from visual information only.

robots↗

NASA NTRS2005

Learning for Autonomous Navigation

Robotic ground vehicles for outdoor applications have achieved some remarkable successes, notably in autonomous highway following (Dickmanns, 1987), planetary exploration (1), and off-road navigation on Earth (1). Nevertheless, major challenges remain to enable reliable, high-speed, autonomous navigation in a wide variety of complex, off-road terrain. 3-D perception of terrain geometry with imaging range sensors is the mainstay of off-road driving systems. However, the stopping distance at high speed exceeds the effective lookahead distance of existing range sensors. Prospects for extending the range of 3-D sensors is strongly limited by sensor physics, eye safety of lasers, and related issues. Range sensor limitations also allow vehicles to enter large cul-de-sacs even at low speed, leading to long detours. Moreover, sensing only terrain geometry fails to reveal mechanical properties of terrain that are critical to assessing its traversability, such as potential for slippage, sinkage, and the degree of compliance of potential obstacles. Rovers in the Mars Exploration Rover (MER) mission have got stuck in sand dunes and experienced significant downhill slippage in the vicinity of large rock hazards. Earth-based off-road robots today have very limited ability to discriminate traversable vegetation from non-traversable vegetation or rough ground. It is impossible today to preprogram a system with knowledge of these properties for all types of terrain and weather conditions that might be encountered.

mixture of Gaussians↗

NASA NTRS2005

Learning for autonomous navigation

robotics↗

NASA NTRS2005

Learning for autonomous navigation : extrapolating from underfoot to the far field

Autonomous off-road navigation of robotic ground vehicles has important applications on Earth and in space exploration. Progress in this domain has been retarded by the limited lookahead range of 3-D sensors and by the difficulty of preprogramming systems to understand the traversability of the wide variety of terrain they can encounter. Enabling robots to learn from experience may alleviate both of these problems. We define two paradigms for this, learning from 3-D geometry and learning from proprioception, and describe initial instantiations of them we have developed under DARPA and NASA programs. Field test results show promise for learning traversability of vegetated terrain, learning to extend the lookahead range of the vision system, and learning how slip varies with slope.

autonomous robots↗