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 53

Assistive Formation Maintenance for Human-Led Multi-Robot Systems

(Georgia Institute of Technology, 2009-10) Parker, Lonnie T. ; Howard, Ayanna M.

In ground-based military maneuvers, group formations require flexibility when traversing from one point to the next. For a human-led team of semi-autonomous agents, a certain level of awareness demonstrated by the agents regarding the quality of the formation is preferable. Through the use of a Multi-Robot System (MRS), this work combines leader-follower principles augmented by an assistive formation maintenance (AFM) method to improve formation keeping and demonstrate a formation-in-motion concept. This is achieved using the Robot Mean Task Allocation method (RTMA), a strategy used to allocate formation positions to each unit within a continuously mobile MRS. The end goal is to provide a military application that allows a soldier to efficiently tele-operate a semi-autonomous MRS capable of holding formation amidst a cluttered environment. Baseline simulation is performed in Player/Stage to show the applicability of our developed model and its potential for expansive research.
Towards Visual Arctic Terrain Assessment

(Georgia Institute of Technology, 2009-07) Williams, Stephen ; Howard, Ayanna M.

Many important scientific studies, particularly those involving climate change, require weather measurements from the ice sheets in Greenland and Antarctica. Due to the harsh and dangerous conditions of such environments, it would be advantageous to deploy a group of autonomous, mobile weather sensors, rather than accepting the expense and risk of human presence. For such a sensor network to be viable, a method of navigating, and thus a method of terrain assessment, must be developed that is tailored for arctic hazards. An extension to a previous arctic terrain assessment method is presented, which is able to produce dense terrain slope estimates from a single camera. To validate this methodology, a set of prototype arctic rovers have been designed, constructed, and fielded on a glacier in Alaska.
Improving the performance of ANN training with an unsupervised filtering method

(Georgia Institute of Technology, 2009-06) Remy, Sekou ; Park, Chung Hyuk ; Howard, Ayanna M.

Learning control strategies from examples has been identified as an important capability for many robotic systems. In this work we show how the learning process can be aided by autonomously filtering the training set provided to improve key properties of the learning process. Demonstrated with data gathered for manipulation tasks, the results herein show the improved performance when autonomous filtering is applied. The filtration method, with no prior knowledge of the task was able to partition the training sets into sets almost equal to expertly labeled sets. In the case where the filter did not produce the same groupings as the expert user, the method still permitted a controller to be trained which demonstrated a success rate of 92%.
Mobility Reconfiguration for Terrain Exploration using Human Inspired Perception

(Georgia Institute of Technology, 2009-05) Brooks, Douglas Antwonne ; Howard, Ayanna M.

The ability of robotic units to autonomously navigate various terrains is critical to the advancement of robotic operation in natural environments. Next generation robots will need to adapt to their environment in order to accomplish tasks that are either too hazardous, too time consuming, or physically impossible for human-beings. Such tasks may include accurate and rapid explorations of various planets or potentially dangerous areas on Earth. Furthermore, because terrain variability typically increases as the distance that a rover traverses increases, it will be beneficial for robotic units to adapt to their surroundings. As a result, this research investigates a navigation control methodology for a multi-modal locomotive robot based upon passive perception. Surface estimation for robot reconfigurability is implemented using a region growing method, which characterizes the traversability of the terrain, in conjunction with passive perception regarding motion. A mathematical approach is then implemented that inherits human psychological aspects to direct necessary navigation behavior to control robot mobility. Physical experimentations in a simulated Mars yard are presented to validate the methodology.
A Probabilistic Model for the Performance Analysis of a Distributed Task Allocation Algorithm

(Georgia Institute of Technology, 2009-05) Viguria Jimenez, Luis Antidio ; Howard, Ayanna M.

In this paper we extend our previous work where the mean of the global cost was used as a performance metric for distributed task allocation algorithms. In this case, we move a step forward and calculate the variance of the global cost. This second parameter gives us a better understanding of the distributed algorithm performance, i.e., we can estimate how much the algorithm behavior diverts from its mean. The normal distribution, computed from the theoretical mean and variance, is shown to be suitable for modeling the global cost. This approximation enables us to compare our algorithm theoretically in different cases.
Playing with Toys: Towards Autonomous Robot Manipulation for Therapeutic Play.

(Georgia Institute of Technology, 2009-05) Trevor, Alexander J. B. ; Park, Hae Won ; Howard, Ayanna M. ; Kemp, Charles C.

When young children play, they often manipulate toys that have been specifically designed to accommodate and stimulate their perceptual-motor skills. Robotic playmates capable of physically manipulating toys have the potential to engage children in therapeutic play and augment the beneficial interactions provided by overtaxed care givers and costly therapists. To date, assistive robots for children have almost exclusively focused on social interactions and teleoperative control. Within this paper we present progress towards the creation of robots that can engage children in manipulative play. First, we present results from a survey of popular toys for children under the age of 2 which indicates that these toys share simplified appearance properties and are designed to support a relatively small set of coarse manipulation behaviors. We then present a robotic control system that autonomously manipulates several toys by taking advantage of this consistent structure. Finally, we show results from an integrated robotic system that imitates visually observed toy playing activities and is suggestive of opportunities for robots that play with toys.
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.
Extracting Play Primitives for a Robot Playmate by Sequencing Low-Level Motion Behavior

(Georgia Institute of Technology, 2008-08) Howard, Ayanna M. ; Park, Hae Won ; Kemp, Charles C.

In this paper, we discuss a methodology to extract play primitives, defined as a sequence of low-level motion behaviors identified during a playing action, such as stacking or inserting a toy. Our premise is that if a robot could interpret the basic movements of a humanpsilas play, it will be able to interact with many different kinds of toys, in conjunction with its human playmate. As such, we present a method that combines motion behavior analysis and behavior sequencing, which capitalizes on the inherent characteristics found in the dynamics of play such as the limited domain of the objects and manipulation skills required. In this paper, we give details on the approach and present results from applying the methodology to a number of play scenarios.
Quantifying Coherence when Learning Behaviors via Teleoperation

(Georgia Institute of Technology, 2008-08) Remy, Sekou ; Howard, Ayanna M.

Applications of robotics are quickly changing. Just as computer use evolved from research purposes to everyday functions, applications of robotics are making a transition to mainstream usage. With this change in applications comes a change in the user base of robotics, and there is a pronounced move to reduce the complexity of robotic control. The move to reduce complexity is linked to the separation of the role of robot designer and robot operator. For many target applications, the operator of the robot needs to be able to correct and augment its capabilities. One method to enable this is learning from human data, which has already been successfully applied to robotics. We assert that this learning process is only viable when the demonstrated human behavior is coherent. In this work we test the hypothesis that quantifying the coherence in the provided instruction can provide useful information about the progress of the learning process. We discuss results from the application of this method to reactive behaviors. Such behaviors permit the learning process to be computationally tractable in real-time. These results support the hypothesis that coherence is important for this type of learning and also show that this property can be used to provide an avenue for self regulation of the learning process.
A Robotic Mobile Sensor Network for Achieving Scientific Measurements in Challenging Environments

(Georgia Institute of Technology, 2008-06) Williams, Stephen ; Viguria Jimenez, Luis Antidio ; Howard, Ayanna M.

Recently, it has been discovered that the giant ice sheets covering Greenland and Antarctica have been shrinking at an accelerated rate. While it is believed that these regions hold important information related to global climate change, there is still insufficient data to be able to accurately predict the future behavior of those ice sheets. Satellites have been able to map the ice sheet elevations with increasing accuracy, but data about general weather conditions (i.e. wind speed, barometric pressure, etc.) must be measured at the surface. A mobile, reconfigurable sensor network would allow the collection of this data without the expense or danger of human presence. For this to be a viable solution though, a method must be developed to allow multiple robotic scientific explorers to navigate these arctic environments. Specific technological achievements that must be addressed for deployment of this surface-based mobile science network include estimating terrain characteristics of the arctic environment, incorporating these characteristics into a robot navigation scheme, and using this scheme to deploy multiple robotic scientific explorers to specific science sites of interest. In this paper, we discuss an infrastructure that addresses these issues in order to enable successful deployment of these robotic scientific explorers.