Person:

Howard, Ayanna M.

Permanent Link

https://hdl.handle.net/1853/72195

Associated Organization(s)

Organizational Unit

School of Interactive Computing

Organizational Unit

School of Electrical and Computer Engineering

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Publication Search Results

Now showing 1 - 10 of 10

Mars exploration rover performance as a baseline for flight Rover autonomy technology assessment

(Georgia Institute of Technology, 2005-09) Tunstel, Edward ; Howard, Ayanna M. ; Maimone, Mark ; Trebi-Ollennu, Ashitey

Technology assessments rely on performance metrics to establish a basis for rating technologies. Metrics are also used to measure relative merit of similar technologies to state-of-the-art technology. Functional performance metrics are presented for mobility and robotic arm autonomy exercised on the Mars Exploration Rovers (MER) surface mission thus far. The metrics are used to apply an existing technology assessment method to establish a baseline for assessing future flight rover technologies. The methodology decomposes robotic activities into operational functions and addresses how technologies, based on performance metrics, can be systematically related to increases in science return. Considering the basic mission objective to maximize scientific yield, we can assess how relative performance of future technologies might impact science return. We provide a useful set of metrics and present an example application of the method to assess merit of hypothetical future Mars rover performance relative to the MER baseline.
A Self-Contained Traversability Sensor for Safe Mobile Robot Guidance in Unknown Terrain

(Georgia Institute of Technology, 2004-09) Howard, Ayanna M. ; Tunstel, Edward

Autonomous mobile robots capable of intelligent behavior must operate with minimal human interaction, have the capability to utilize local resources, and routinely make closed-loop decisions in real-time based on sensor data inputs. One of the bottlenecks in achieving this is an often computationally intensive perception process. In this paper, we discuss a class of cognitive sensor devices capable of intelligent perception that can facilitate intelligent behavior. The primary emphasis is on achieving safe mobile guidance for planetary exploration by distributing some of the perception functionality to self-contained sensors. An example cognitive sensor, called the traversability sensor, is presented, which consists of a camera and embedded processor coupled with an intelligent visual perception algorithm. The sensor determines local terrain traversability in natural outdoor environments and, accordingly, directs movement of a mobile robot toward the safest visible terrain area in a self-contained fashion, placing minimal burden on the main processor. A cognitive sensor design approach is presented and a traversability sensor prototype is described.
Approximate Reasoning for Safety and Survivability of Planetary Rovers

(Georgia Institute of Technology, 2003-02) Tunstel, Edward ; Howard, Ayanna M.

Operational safety and health monitoring are critical matters for autonomous planetary rovers operating on remote and challenging terrain. This paper describes rover safety issues and presents an approximate reasoning approach to maintaining vehicle safety in a navigational context. The proposed rover safety module is composed of two distinct behaviors: safe attitude (pitch and roll) management and safe traction management. Fuzzy logic implementations of these behaviors on outdoor terrain is presented. Sensing of vehicle safety coupled with visual neural network-based perception of terrain quality are used to infer safe speeds during rover traversal. In addition, approximate reasoning for self-regulation of internal operating conditions is briefly discussed. The core theoretical foundations of the applied soft computing techniques is presented and supported by descriptions of field tests and laboratory experimental results. For autonomous rovers, the approach provides intrinsic safety cognizance and a capacity for reactive mitigation of navigation risks.
Rule-based reasoning and neural network perception for safe off-road robot mobility

(Georgia Institute of Technology, 2002-09) Tunstel, Edward ; Howard, Ayanna M. ; Seraji, Homayoun

Operational safety and health monitoring are critical matters for autonomous field mobile robots such as planetary rovers operating on challenging terrain. This paper describes relevant rover safety and health issues and presents an approach to maintaining vehicle safety in a mobility and navigation context. The proposed rover safety module is composed of two distinct components: safe attitude (pitch and roll) management and safe traction management. Fuzzy logic approaches to reasoning about safe attitude and traction management are presented, wherein inertial sensing of safety status and vision-based neural network perception of terrain quality are used to infer safe speeds of traversal. Results of initial field tests and laboratory experiments are also described. The approach provides an intrinsic safety cognizance and a capacity for reactive mitigation of robot mobility and navigation risks.
Sensing and perception challenges of planetary surface robotics

(Georgia Institute of Technology, 2002-06) Tunstel, Edward ; Howard, Ayanna M.

This expository paper describes sensing and perception issues facing the space robotics community concerned with deploying autonomous rovers on other planetary surfaces. Challenging sensing problems associated with rover surface navigation and manipulation functions are discussed for which practical solutions from sensor developers would vastly improve rover capabilities. Some practical concerns that impact sensor selection based on mass, power, and operability constraints are also discussed. The intent is to present challenges to facilitate alignment of new sensing solutions with key sensing requirements of planetary surface robotics.
Enhancing Fuzzy Robot Navigation Systems by Mimicking Human Visual Perception of Natural Terrain Traversability

(Georgia Institute of Technology, 2001-07) Howard, Ayanna M. ; Tunstel, Edward ; Edwards, Dean ; Carlson, Alan

This paper presents a technique for learning to assess terrain traversability for outdoor mobile robot navigation using human-embedded logic and real-time perception of terrain features extracted from image data. The methodology utilizes a fuzzy logic framework and vision algorithms for analysis of the terrain. The terrain assessment and learning methodology is tested and validated with a set of realworld image data acquired by an onboard vision system.
Terrain-Based Navigation of Planetary Rovers: A Fuzzy Logic Approach

(Georgia Institute of Technology, 2001-06) Seraji, Homayoun ; Howard, Ayanna M. ; Tunstel, Edward

This paper presents a new strategy for autonomous navigation of eld mobile robots on hazardous natural terrain using a fuzzy logic approach and a novel mea- sure of terrain traversability. The navigation strategy is comprised of three simple, independent behaviors: seek-goal, traverse-terrain, and avoid-obstacle. The recommendations from these three behaviors are com- bined through appropriate weighting factors to gen- erate the nal steering and speed commands that are executed by the robot. The weighting factors are pro- duced by fuzzy logic rules that take into account the current status of the robot. This navigation strategy requires no a priori information about the environ- ment, and uses the on-board traversability analysis to enable the robot to select relatively easy-to-traverse paths autonomously. Field test results obtained from implementation of the proposed algorithms on the commercial Pioneer AT rover are presented. These results demonstrate the real-time capabilities of the terrain assessment and fuzzy logic navigation algorithms.
Safe Navigation on Hazardous Terrain

(Georgia Institute of Technology, 2001-05) Seraji, Homayoun ; Howard, Ayanna M. ; Tunstel, Edward

This paper presents a new strategy for autonomous navigation of field mobile robots on hazardous natural terrain using a fuzzy logic approach and a measure of terrain traversability. The navigation strategy comprises three simple, independent behaviors: seek-goal, traverse-terrain, and avoid-obstacle. The recommendations from these three behaviors are combined through appropriate weighting factors to generate the final steering and speed commands that are executed by the robot. The weighting factors are produced by fuzzy logic rules that take into account the current status of the robot. This navigation strategy requires no a priori information about the environment, and uses the on-board traversability analysis to enable the robot to select relatively easy-to-traverse paths autonomously. Field test results obtained from implementation of the proposed algorithms on the commercial Pioneer All Terrain rover are presented. These results demonstrate the real-time capabilities of the terrain assessment and fuzzy logic navigation algorithms.
Fuzzy Rule-Based Reasoning for Rover Safety and Survivability

(Georgia Institute of Technology, 2001-05) Tunstel, Edward ; Howard, Ayanna M. ; Seraji, Homayoun

Operational safety and health monitoring are critical matters for autonomous field mobile robots such as planetary rovers operating on challenging terrain. The paper describes relevant rover safety and health issues and presents an approach to maintaining vehicle safety in a navigational context. The proposed rover safety module is composed of two distinct components: safe attitude (pitch and roll) management and safe traction management. Fuzzy logic approaches to reasoning about safe attitude and traction management are presented, wherein sensing of safety status and perception of terrain quality are used to infer safe speeds of traversal. Results of field tests and laboratory experiments are also described. The approach provides an intrinsic safety cognizance and a capacity for reactive mitigation of navigation risks.
A Rule-Based Fuzzy Traversability Index for Mobile Robot Navigation

(Georgia Institute of Technology, 2001-05) Howard, Ayanna M. ; Seraji, Homayoun ; Tunstel, Edward

This paper presents a rule-based fuzzy traversability index that quantifies the ease-of-traversal of a terrain by a mobile robot based on real-time measurements of terrain characteristics retrieved from imagery data. These characteristics include, but are not limited to slope, roughness, hardness, and discontinuity. The proposed representation of terrain traversability incorporates an intuitive, linguistic approach for expressing terrain characteristics that is robust with respect to imprecision and uncertainty in the terrain measurements. The terrain assessment method is tested and validated with a set of real-world imagery data. These tests demonstrate the capability of the terrain classification algorithm for perceiving hazards associated with terrain traversal.