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 16

A novel tiered sensor fusion approach for terrain characterization and safe landing assessment

(Georgia Institute of Technology, 2006-03) Serrano, Navid ; Bajracharya, Max ; Howard, Ayanna M. ; Seraji, Homayoun

This paper presents a novel, tiered sensor fusion methodology for real-time terrain safety assessment. A combination of active and passive sensors, specifically, radar, lidar, and camera, operate in three tiers according to their inherent ranges of operation. Low-level terrain features (e.g. slope, roughness) and high-level terrain features (e.g. hills, craters) are integrated using principles of reasoning under uncertainty. Three methodologies are used to infer landing safety: fuzzy reasoning, probabilistic reasoning, and evidential reasoning. The safe landing predictions from the three fusion engines are consolidated in a subsequent decision fusion stage aimed at combining the strengths of each fusion methodology. Results from simulated spacecraft descents are presented and discussed.
Global and Regional Path Planners for Integrated Planning and Navigation

(Georgia Institute of Technology, 2005-12) Howard, Ayanna M. ; Seraji, Homayoun ; Werger, Barry

This paper presents a hierarchical strategy for field mobile robots that incorporates path planning at different ranges. At the top layer is a global path planner that utilizes gross terrain characteristics, such as hills and valleys, to determine globally safe paths through the rough terrain. This information is then passed via waypoints to a regional layer that plans appropriate navigation paths using regional terrain characteristics. The global and regional path planners share the same map information, but at different ranges. The motion recommendations from the regional layer are then combined with those of the reactive navigation layer to provide reactive control for the mobile robot. Details of the global and regional path planners are discussed, and simulation and experimental results are presented.
A Human-Robot Mentor-Protégé Relationship to Learn Off-Road Navigation Behavior

(Georgia Institute of Technology, 2005-10) Howard, Ayanna M. ; Werger, Barry ; Seraji, Homayoun

In this paper, we present an approach to transfer human expertise for learning off-road navigation behavior to an autonomous mobile robot. The methodology uses the concept of humanized intelligence to combine principal component analysis and neural network learning to embed human driving expertise onto mobile robots. The algorithms are tested in the field using a commercial Pioneer 2AT robot to demonstrate autonomous traversal over rough natural terrain.
Multi-sensor terrain classification for safe spacecraft landing

(Georgia Institute of Technology, 2004-10) Howard, Ayanna M. ; Seraji, Homayoun

A novel multi-sensor information fusion methodology for intelligent terrain classification is presented. The focus of this research is to analyze safety characteristics of the terrain using imagery data obtained by on-board sensors during spacecraft descent. This information can be used to enable the spacecraft to land safely on a planetary surface. The focus of our approach is on robust terrain analysis and information fusion in which the terrain is analyzed using multiple sensors and the extracted terrain characteristics are combined to select safe landing sites for touchdown. The novelty of this method is the incorporation of the T-Hazard Map, a multi-valued map representing the risk associated with landing on a planetary surface. The fusion method is explained in detail in this paper and computer simulation results are presented to validate the approach.
Integrating Terrain Maps into a Reactive Navigation Strategy

(Georgia Institute of Technology, 2003-09) Howard, Ayanna M. ; Werger, Barry ; Seraji, Homayoun

This paper presents a new method for integrating terrain maps into a reactive navigation strategy of field mobile robots operating on rough terrain. The method incorporates the Regional Traversability Map, a fuzzy map representation of traversal difficulty of the regional terrain, into the navigation logic. A map-based regional navigation behavior provides speed and direction recommendations based on the current status of the robot. In addition, recommendations from two sensor-based reactive behaviors, local avoid-obstacle and regional traverse-terrain, are fused with the map-based regional behavior to construct a comprehensive navigation system. The algorithms are tested both in graphical simulations and in the field using a commercial Pioneer 2AT robot to demonstrate traversal over rough natural terrain.
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.
Behavior-Based Robot Navigation on Challenging Terrain: A Fuzzy Logic Approach

(Georgia Institute of Technology, 2002-06) Seraji, Homayoun ; Howard, Ayanna M.

This paper presents a new strategy for behavior-based navigation of field mobile robots on challenging terrain, using a fuzzy logic approach and a novel measure of terrain traversability. A key feature of the proposed approach is real-time assessment of terrain characteristics and incorporation of this information in the robot navigation strategy. Three terrain characteristics that strongly affect its traversability, namely, roughness, slope, and discontinuity, are extracted from video images obtained by on-board cameras. This traversability data is used to infer, in real time, the terrain Fuzzy Rule-Based Traversability Index, which succinctly quantifies the ease of traversal of the regional terrain by the mobile robot. A new traverse-terrain behavior is introduced that uses the regional traversability index to guide the robot to the safest and the most traversable terrain region. The regional traverse-terrain behavior is complemented by two other behaviors, local avoid-obstacle and global seek-goal. The recommendations of these three behaviors are integrated through adjustable weighting factors to generate the final motion command for the robot. The weighting factors are adjusted automatically, based on the situational context of the robot. The terrain assessment and robot navigation algorithms Are implemented on a Pioneer commercial robot and field-test studies are conducted. These studies demonstrate that the robot possesses intelligent decision-making capabilities that are brought to bear in negotiating hazardous terrain conditions during the robot motion.
A Rule-Based Fuzzy Safety Index for Landing Site Risk Assessmen

(Georgia Institute of Technology, 2002-06) Howard, Ayanna M. ; Seraji, Homayoun

This paper presents a fuzzy rule-based safety index that quantifies the ease-of-landing a spacecraft on a planetary surface based on sensor-derived measurements of terrain characteristics. These characteristics include, but are not limited to, slope and roughness. The proposed representation of terrain safety incorporates an intuitive, linguistic approach for expressing terrain characteristics that is robust with respect to imprecision and uncertainty in the sensor measurements. The risk assessment methodology is tested and validated with a set of simulated data. These tests demonstrate the capability of the algorithm for perceiving hazards associated with landing on a planetary surface.
An intelligent terrain-based navigation system for planetary rovers

(Georgia Institute of Technology, 2001-12) Howard, Ayanna M. ; Seraji, Homayoun

A fuzzy logic framework for onboard terrain analysis and guidance towards traversable regions. An onboard terrain-based navigation system for mobile robots operating on natural terrain is presented. This system utilizes a fuzzy-logic framework for onboard analysis of the terrain and develops a set of fuzzy navigation rules that guide the rover toward the safest and the most traversable regions. The overall navigation strategy deals with uncertain knowledge about the environment and uses the onboard terrain analysis to enable the rover to select easy-to-traverse paths to the goal autonomously. The navigation system is tested and validated with a set of physical rover experiments and demonstrates the autonomous capability of the system.
Vision-Based Terrain Characterization and Traversability Assessment

(Georgia Institute of Technology, 2001-10) Howard, Ayanna M. ; Seraji, Homayoun

This article presents novel techniques for real-time terrain characterization and assessment of terrain traversability for a field mobile robot using a vision system and artificial neural networks. The key terrain traversability characteristics are identified as roughness, slope, discontinuity, and hardness. These characteristics are extracted from imagery data obtained from cameras mounted on the robot and are represented in a fuzzy logic framework using perceptual, linguistic fuzzy sets. The approach adopted is highly robust and tolerant to imprecision and uncertainty inherent in sensing and perception of natural environments. The four traversability characteristics are combined to form a single Fuzzy Traversability Index, which quantifies the ease-of-traversal of the terrain by the mobile robot. Experimental results are presented to demonstrate the capability of the proposed approach for classification of different terrain segments based on their traversability