Organizational Unit:

Institute for Robotics and Intelligent Machines (IRIM)

Permanent Link

https://hdl.handle.net/1853/70657

Includes Organization(s)

Organizational Unit

Biorobotics and Human Modeling Lab

Organizational Unit

Humanoid Robotics Laboratory

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Publication Search Results

Now showing 1 - 10 of 12

Assessment of Engagement for Intelligent Educational Agents: A Pilot Study with Middle School Students

(Georgia Institute of Technology, 2014) Brown, LaVonda ; Howard, Ayanna M.

Adaptive learning is an educational method that utilizes computers as an interactive teaching device. Intelligent tutoring systems, or educational agents, use adaptive learning techniques to adapt to each student’s needs and learning styles in order to individualize learning. Effective educational agents should accomplish two essential goals during the learning process – 1) monitor engagement of the student during the interaction and 2) apply behavioral strategies to maintain the student’s attention when engagement decreases. In this paper, we focus on the first objective of monitoring student engagement. Most educational agents do not monitor engagement explicitly, but rather assume engagement and adapt their interaction based on the student’s responses to questions and tasks. A few advanced methods have begun to incorporate models of engagement through vision-based algorithms that assess behavioral cues such as eye gaze, head pose, gestures, and facial expressions. Unfortunately, these methods typically require a heavy computation load, memory/storage constraints, and high power consumption. In addition, these behavioral cues do not correlate well with achievement of highlevel cognitive tasks. As an alternative, our proposed model of engagement uses physical events, such as keyboard and mouse events. This approach requires fewer resources and lower power consumption, which is also ideally suited for mobile educational agents such as handheld tablets and robotic platforms. In this paper, we discuss our engagement model which uses techniques that determine behavioral user state and correlate these findings to mouse and keyboard events. In particular, we observe three event processes: total time required to answer a question; accuracy of responses; and proper function executions. We evaluate the correctness of our model based on an investigation involving a middle-school after-school program in which a 15-question math exam that varies in cognitive difficulty is used for assessment. Eye gaze and head pose techniques are referenced for the baseline metric of engagement. We conclude the investigation with a survey to gather the subject’s perspective of their mental state after the exam. We found that our model of engagement is comparable to the eye gaze and head pose techniques for low-level cognitive tasks. When high-level cognitive thinking is required, our model is more accurate than the eye gaze and head pose techniques due to the students’ nonfocused gazes during questions requiring deep thought or use of outside variables for assistance such as their fingers to count. The large time delay associated with the lack of eye contact between the student and the computer screen causes the aforementioned algorithms to incorrectly declare the subjects as being disengaged. Furthermore, speed and validity of responses can help to determine how well the student understands the material, and this is confirmed through the survey responses and video observations. This information will be used later to integrate instructional scaffolding and adaptation with the educational agent.
Terrain Reconstruction of Glacial Surfaces via Robotic Surveying Techniques

(Georgia Institute of Technology, 2012-12) Williams, Stephen ; Parker, Lonnie T. ; Howard, Ayanna M.

The capability to monitor natural phenomena using mobile sensing is a benefit to the Earth science community given the potentially large impact that we, as humans, can have on naturally occurring processes. Observable phenomena that fall into this category of interest range from static to dynamic in both time and space (i.e. temperature, humidity, and elevation). Such phenomena can be readily monitored using networks of mobile sensor nodes that are tasked to regions of interest by scientists. In our work, we hone in on a very specific domain, elevation changes in glacial surfaces, to demonstrate a concept applicable to any spatially distributed phenomena. Our work leverages the sensing of a vision-based SLAM odometry system and the design of robotic surveying navigation rules to reconstruct scientific areas of interest, with the goal of monitoring elevation changes in glacial regions. We validate the output from our methodology and provide results that show the reconstructed terrain error complies with acceptable mapping standards found in the scientific community.
Using Haptic and Auditory Interaction Tools to Engage Students with Visual Impairments in Robot Programming Activities

(Georgia Institute of Technology, 2012-01) Howard, Ayanna M. ; Park, Chung Hyuk ; Remy, Sekou

The robotics field represents the integration of multiple facets of computer science and engineering. Robotics-based activities have been shown to encourage K-12 students to consider careers in computing and have even been adopted as part of core computer-science curriculum at a number of universities. Unfortunately, for students with visual impairments, there are still inadequate opportunities made available for teaching basic computing concepts using robotics-based curriculum. This outcome is generally due to the scarcity of accessible interfaces to educational robots and the unfamiliarity of teachers with alternative (e.g., nonvisual) teaching methods. As such, in this paper, we discuss the use of alternative interface modalities to engage students with visual impairments in robotics-based programming activities. We provide an overview of the interaction system and results on a pilot study that engaged nine middle school students with visual impairments during a two-week summer camp.
Calibration and Validation of Earth-Observing Sensors Using Deployable Surface-Based Sensor Networks

(Georgia Institute of Technology, 2010-12) Williams, Stephen ; Parker, Lonnie T. ; Howard, Ayanna M.

Satellite-based instruments are now routinely used to map the surface of the globe or monitor weather conditions. However, these orbital measurements of ground-based quantities are heavily influenced by external factors, such as air moisture content or surface emissivity. Detailed atmospheric models are created to compensate for these factors, but the satellite system must still be tested over a wide variety of surface conditions to validate the instrumentation and correction model. Validation and correction are particularly important for arctic environments, as the unique surface properties of packed snow and ice are poorly modeled by any other terrain type. Currently, this process is human intensive, requiring the coordinated collection of surface measurements over a number of years. A decentralized, autonomous sensor network is proposed which allows the collection of ground-based environmental measurements at a location and resolution that is optimal for the specific on-orbit sensor under investigation. A prototype sensor network has been constructed and fielded on a glacier in Alaska, illustrating the ability of such systems to properly collect and log sensor measurements, even in harsh arctic environments.
An Integrated Approach for Achieving Multi-Robot Task Formations

(Georgia Institute of Technology, 2009-04) Viguria Jimenez, Luis Antidio ; Howard, Ayanna M.

In this paper, a problem, called the initial formation problem, within the multirobot task allocation domain is addressed. This problem consists in deciding which robot should go to each of the positions of the formation in order to minimize an objective. Two different distributed algorithms that solve this problem are explained. The second algorithm presents a novel approach that uses cost means to model the cost distribution and improves the performance of the task allocation algorithm. Also, we present an approach that integrates distributed task allocation algorithms with a behavior-based architecture to control formations of robot teams. Finally, simulations and real experiments are used to analyze the formation behavior and provide performance metrics associated with implementation in realistic scenarios.
A Systematic Approach to Predict Performance of Human-Automation Systems

(Georgia Institute of Technology, 2007-07) Howard, Ayanna M.

This paper discusses an approach for predicting system performance resulting from humans and robots performing repetitive tasks in a collaborative manner. The methodology uses a systematic approach that incorporates the various effects of workload on human performance, and estimates resulting performance attributes derived between teleoperated and autonomous control of robotic systems. Performance is determined by incorporating capabilities of the human and robotic agent based on accomplishment of functional operations and effect of cognitive stress due to continuous operation by the human agent. This paper provides an overview of the prediction system and discusses its implementation on a simulated rendezvous/docking task.
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.
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.
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.