Author name cluster

Alan C. Schultz

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13 papers

2 author rows

ICRA Conference 2007 Conference Paper

Robotic Discovery of the Auditory Scene

Eric Martinson
Alan C. Schultz

In this work, we describe an autonomous mobile robotic system for finding and investigating ambient noise sources in the environment. Motivated by the large negative effect of ambient noise sources on robot audition, the long-term goal is to provide awareness of the auditory scene to a robot, so that it may more effectively act to filter out the interference or re-position itself to increase the signal-to-noise ratio. Here, we concentrate on the discovery of new sources of sound through the use of mobility and directed investigation. This is performed in a two-step process. In the first step, a mobile robot first explores the surrounding acoustical environment, creating evidence grid representations to localize the most influential sound sources in the auditory scene. Then in the second step, the robot investigates each potential sound source location in the environment so as to improve the localization result, and identify volume and directionality characteristics of the sound source. Once every source has been investigated, a noise map of the entire auditory scene is created for use by the robot in avoiding areas of loud ambient noise when performing an auditory task.

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AAAI Conference 2007 Conference Paper

Spatial Representation and Reasoning for Human-Robot Collaboration

William G. Kennedy
Matthew Marge
Benjamin R. Fransen
Alan C. Schultz

How should a robot represent and reason about spatial information when it needs to collaborate effectively with a human? The form of spatial representation that is useful for robot navigation may not be useful in higher-level reasoning or working with humans as a team member. To explore this question, we have extended previous work on how children and robots learn to play hide and seek to a human-robot team covertly approaching a moving target. We used the cognitive modeling system, ACT-R, with an added spatial module to support the robot’s spatial reasoning. The robot interacted with a team member through voice, gestures, and movement during the team’s covert approach of a moving target. This paper describes the new robotic system and its integration of metric, symbolic, and cognitive layers of spatial representation and reasoning for its individual and team behavior.

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IROS Conference 2006 Conference Paper

Auditory Evidence Grids

Eric Martinson
Alan C. Schultz

Sound source localization on a mobile robot can be a difficult task due to a variety of problems inherent to a real environment, including robot ego-noise, echoes, and the transient nature of ambient noise. As a result, source localization data are often very noisy and unreliable. In this work, we overcome some of these problems by combining the localization evidence over a variety of robot poses using an evidence grid. The result is a representation that localizes the pertinent objects well over time, can be used to filter poor localization results, and may also be useful for global re-localization from sound localization results

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IROS Conference 2006 Conference Paper

Continuous and Embedded Learning for Multi-Agent Systems

Zsolt Kira
Alan C. Schultz

This paper describes multi-agent strategies for applying continuous and embedded learning (CEL). In the CEL architecture, an agent maintains a simulator based on its current knowledge of the world and applies a learning algorithm that obtains its performance measure using this simulator. The simulator is updated to reflect changes in the environment or robot state that can be detected by a monitor, such as sensor failures. In this paper, we adapt this architecture to a multi-agent setting in which the monitor is communicated among the team members effectively creating a distributed monitor. The parameters of the current control algorithm (in our case rulebases learned by genetic algorithms) used by all of the agents are added to the monitor as well, allowing for cooperative learning. We show that communication of agent status (e. g. failures) among the team members allows the agents to dynamically adapt to team properties, in this case team size. Furthermore, we show that an agent is able to switch between specializing within a section of the domain when there are many team members and generalizing to other parts of the domain when the rest of the team members are disabled. Finally, we also discuss future potential of this method, most notably in the creation of a distributed case based reasoning system in which the cases are actual genetic algorithm population members that can be swapped among team members

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ICRA Conference 2006 Conference Paper

Using a Qualitative Sketch to Control a Team of Robots

Marjorie Skubic
Derek Anderson
Samuel Blisard
Dennis Perzanowski
Alan C. Schultz

In this paper, we describe a prototype interface that facilitates the control of a mobile robot team by a single operator, using a sketch interface on a tablet PC. The user sketches a qualitative map of the scene and includes the robots in approximate starting positions. Both path and target position commands are supported as well as editing capabilities. Sensor feedback from the robots is included in the display such that the sketch interface acts as a two-way communication device between the user and the robots. The paper also includes results of a usability study, in which users were asked to perform a series of tasks

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IROS Conference 2005 Conference Paper

Designing robots for long-term social interaction

Rachel Gockley
Allison Bruce
Jodi Forlizzi
Marek P. Michalowski
Anne Mundell
Stephanie Rosenthal
Brennan Sellner
Reid G. Simmons

Valerie the roboceptionist is the most recent addition to Carnegie Mellon's social robots project. A permanent installation in the entranceway to Newell-Simon hall, the robot combines useful functionality - giving directions, looking up weather forecasts, etc. - with an interesting and compelling character. We are using Valerie to investigate human-robot social interaction, especially long-term human-robot "relationships". Over a nine-month period, we have found that many visitors continue to interact with the robot on a daily basis, but that few of the individual interactions last for more than 30 seconds. Our analysis of the data has indicated several design decisions that should facilitate more natural human-robot interactions.

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IROS Conference 2003 Conference Paper

Representing a 3-D environment with a 2 1/2 -D map structure

Edward H. L. Fong
William Adams
Frederick L. Crabbe
Alan C. Schultz

This paper explores the development of a two and one-half dimensional (2 1/2 -D) map structure to provide an autonomous mobile robot with a more three-dimensional (3-D) model of its environment than those afforded by current map structures. The 2 1/2 -D map structure was created by modifying the widely used evidence grid to store a height, along with a probability value, in each cell location to record the varying elevations of a 3-D environment. Results show that this map structure is capable of providing an autonomous mobile robot with a representation of a limited 3-D environment that will allow it to perform obstacle detection, path planning, and to an extent, localization.

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IROS Conference 2002 Conference Paper

A hybrid cognitive-reactive multi-agent controller

Magdalena D. Bugajska
Alan C. Schultz
J. Greg Trafton
Matthew Taylor
Farilee Mintz

The purpose of this paper is to introduce a hybrid cognitive-reactive system, which integrates a machine-learning algorithm (SAMUEL, an evolutionary algorithm-based rule-learning system) with a computational cognitive model (written in ACT-R). In this system, the learning algorithm handles reactive aspects of the task and provides an adaptation mechanism, while the cognitive model handles cognitive aspects of the task and ensures the realism of the behavior. In this study, the controller architecture is used to implement a controller for a team of micro-air vehicles performing reconnaissance and surveillance.

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ICRA Conference 2002 Conference Paper

Using Spatial Language in a Human-Robot Dialog

Marjorie Skubic
Dennis Perzanowski
Alan C. Schultz
William Adams

In conversation, people often use spatial relationships to describe their environment, e. g. , "There is a desk in front of me and a doorway behind it", and to issue directives, e. g. , "Go around the desk and through the doorway. " In our research, we have been investigating the use of spatial relationships to establish a natural communication mechanism between people and robots, in particular, for novice users. In this paper, the work on robot spatial relationships is combined with a multimodal robot interface developed at the Naval Research Lab. We show how linguistic spatial descriptions and other spatial information can be extracted from an evidence grid map and how this information can be used in a natural, human-robot dialog.

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AAAI Conference 1999 Conference Paper

A Natural Interface and Unified Skills for a Mobile Robot

William Adams
Dennis Perzanowski
Alan C. Schultz
Naval Research Laboratory

Our research is aimed at developing an independent, cooperative, autonomous agent. Toward this end, we are working on two areas: a natural interface for interacting with the robot, and the basic underlying skills for navigating in previously unknown environments.

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ICRA Conference 1999 Conference Paper

Unifying Exploration, Localization, Navigation, and Planning Through a Common Representation

Alan C. Schultz
William Adams
Brian Yamauchi
Mike Jones

The major themes of our research include the creation of mobile robot systems that are robust and adaptive in rapidly changing environments and the view of integration as a basic research issue. Where reasonable, we try to use the same representations to allow different components to work more readily together and to allow better and more natural integration of and communication between these components. In this paper, we describe our most recent work in integrating mobile robot exploration, localization, navigation, and planning through the use of a common representation, evidence grids.

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ICRA Conference 1998 Conference Paper

Continuous Localization Using Evidence Grids

Alan C. Schultz
William Adams

Evidence grids provide a uniform representation for fusing temporally and spatially distinct sensor readings. However, the use of evidence grids requires that the robot be localized within its environment. Odometry errors typically accumulate over time, making localization estimates degrade, and introducing significant errors into evidence grids as they are built. We have addressed this problem by developing a method for "continuous localization", in which the robot corrects its localization estimates incrementally and on the fly. Assuming the mobile robot has a map of its environment represented as an evidence grid, localization is achieved by building a series of "local perception grids" based on localized sensor readings and the current odometry, and then registering the local and global grids. The registration produces an offset which is used to correct the odometry. Results are given on the effectiveness of this method, and quantify the improvement of continuous localization over dead reckoning. We also compare different techniques for matching evidence grids and for searching registration offsets.

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ICRA Conference 1998 Conference Paper

Mobile Robot Exploration and Map-Building with Continuous Localization

Brian Yamauchi
Alan C. Schultz
William Adams

Our research addresses how to integrate exploration and localization for mobile robots. A robot exploring and mapping an unknown environment needs to know its own location, but it may need a map in order to determine that location. In order to solve this problem, we have developed ARIEL, a mobile robot system that combines frontier based exploration with continuous localization. ARIEL explores by navigating to frontiers, regions on the boundary between unexplored space and space that is known to be open. ARIEL finds these regions in the occupancy grid map that it builds as it explores the world. ARIEL localizes by matching its recent perceptions with the information stored in the occupancy grid. We have implemented ARIEL on a real mobile robot and tested ARIEL in a real-world office environment. We present quantitative results that demonstrate that ARIEL can localize accurately while exploring, and thereby build accurate maps of its environment.

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