Author name cluster

Amy C. Larson

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

1 author row

IROS Conference 2007 Conference Paper

Evolving gaits for increased discriminability in terrain classification

Amy C. Larson
Richard M. Voyles
Jaewook Bae
Roy Godzdanker

Limbs are an attractive approach to certain niche robotic applications, such as urban search and rescue, that require both small size and the ability to locomote through highly rubbled terrain. Unfortunately, a large number of degrees of freedom implies there is a large space of non- optimal locomotion trajectories (gaits), making gait adaptation critical. On the other hand, these extra degrees of freedom open many possibilities for active sensing of the terrain, which is essential information for adapting the gait. In previous work, we developed a metric for terrain classification that makes use of the loping body motion (i. e. gait bounce) during locomotion. In this work we present a framework for evolving gaits to better differentiate the gait bounce signal across terrains. This framework includes a limb/terrain interaction model that estimates gait bounce based on established models of wheel/terrain interaction, and an objective function that can be optimized for terrain discriminability. Additional objective functions for improved locomotion are presented, as well as culling agents that help guide the evolution process away from real-world impossibilities.

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IROS Conference 2004 Conference Paper

Core-bored search-and-rescue applications for an agile limbed robot

Richard M. Voyles
Amy C. Larson
Jaewook Bae
Monica A. LaPoint

A custom version of the TerminatorBot is described for core bored inspection during search-and-rescue operations. "Core bored inspection" refers to visual inspection of a void by passing a small camera through an access hole into the void. This is the classic "camera-on-a-stick" approach. Sometimes the access hole occurs naturally. Sometimes a suspected void has no access hole. To gain access, a hole is bored through the rubble with a coring tool, hence the term "core-bored inspection". In either case, the camera, once inside, can articulate to look around, but is limited to fine-of-sight. Occlusions can prevent a thorough inspection or force using/boring another hole. A small, agile robotic device could augment the use of such cameras. We propose the TerminatorBot as a prototype limbed robot for studying such applications.

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IROS Conference 2004 Conference Paper

Motion estimation with cooperatively working multiple robots

Güleser Kalayci Demir
Richard M. Voyles
Amy C. Larson

We have investigated the performance of simultaneously estimating the 3D motion and structure for navigation when the scale information is obtained by utilizing the cooperative efforts of multiple robots. The method determines the relative positions of robots by tracking a specific geometric feature that is part of their structure, and then uses the extended Kalman filter to estimate the motion and structure. For implementation we used two CRAWLER Scouts, and performed several experiments to explore the effects of cooperative running of robots on the motion estimation.

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ICRA Conference 2004 Conference Paper

Terrain Classification through Weakly-structured Vehicle/terrain Interaction

Amy C. Larson
Richard M. Voyles
Güleser Kalayci Demir

We present a new terrain classification technique both for effective, autonomous locomotion over natural, unknown terrains and for the qualitative analysis of terrains for exploration and mapping. Our straight-forward approach requires a single camera with little processing of visual information. Specifically, we derived a gait bounce measure from visual servoing errors that result from vehicle-terrain interactions during normal locomotion. Characteristics of the terrain, such as roughness and compliance, manifest themselves in the spatial patterns of this signal and can be extracted using pattern classification techniques. For legged robots, different limb-terrain interactions generate gait bounce signals with different information content, thus deliberate limb motions can effect higher information content (i. e. the robot is an active sensor of terrain class). Segmentation of the gait cycle based on the limb-terrain interaction isolates portions of the gait bounce signal with high information content. The decoding of, then sequencing of, this content from each cycle segment yields a robust classification of terrain type from known benchmarks. To extract this spatio-temporal pattern of the gait bounce signal, we developed a meta-classifier using discriminant analysis and hidden Markov model. We present the gait bounce derivation. We demonstrate the viability of terrain classification for legged vehicles using gait bounce with a rigorous study of more than 700 trials, obtaining 84% accuracy. We describe how terrain classification can be used for gait adaptation, particularly in relation to an efficiency metric. We also demonstrate that our technique is generally applicable to other locomotion mechanisms such as wheels and treads.

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IROS Conference 2001 Conference Paper

Automatic training data selection for sensorimotor primitives

Amy C. Larson
Richard M. Voyles

Sequencing sensorimotor primitives to achieve complex behaviors can simplify programming of robotic systems. Using programming by demonstration to code the component primitives can further simplify the process. Learning methods employed in programming by demonstration require comprehensive data sets, which place a significant burden on the user during demonstration. We present a generalized method whereby training sets can be automatically filtered, freeing the user from knowledge of the underlying learning method. We achieve this by first capturing the characteristic behavior for a demonstrated task, then determining a measure of distance from that behavior. With this information, data sets can be analyzed to determine whether a particular moment of demonstration is "good" and should be included in the final training set. Results from programming by demonstration of left wall-following on a mobile platform are presented. Additionally, we present a method for on-line performance analysis that takes advantage of the characteristic behavior identified in the filtering process.

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IROS Conference 2001 Conference Paper

Performance evaluation of sensorimotor primitives using eigenvector learning method

Michael S. Sutton
Amy C. Larson
Richard M. Voyles

We present a method to evaluate the performance of an eigenvector learned sensorimotor primitive for mobile robots. At runtime, the learning system projects sensor data onto the eigenspace using eigenvectors determined in training. The result of the projection is a set of sensor values and actuator values. We developed an error metric based on comparing the projected values with the actual sensor values. When the system performs closely to how it was trained, the difference between projected and actual sensors is small and hence the error metric is small. The error increases as the performance degrades. This method is not task specific and can be used for any eigenvector learned primitive. Two example applications of the error metric are shown using wall following skills for a mobile robot. First, the metric is used as a transition cue for multiprimitive sequential tasks. Second, the error metric is used to create an adaptive system that chooses the best performing skill.

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IROS Conference 2001 Conference Paper

Using orthogonal visual servoing errors for classifying terrain

Richard M. Voyles
Amy C. Larson
Kemal Berk Yesin
Bradley J. Nelson

A novel, centimeter-scale crawling robot has been developed to address applications in surveillance, search-and-rescue, and planetary exploration. This places constraints on size and durability that minimizes the mechanism. As a result, a dual-use design employing two arms for both manipulation and locomotion was conceived. In a complementary fashion, this paper investigates the dual-use of visual servoing error. Visual servoing can be used by a mobile robot for homing and tracking. But because ground-based mobile robots are inherently planar, the control methodology (steering) is one-dimensional. The two-dimensional nature of image-based servoing leaves additional information content to be used in other contexts. We explore this information in the context of classifying terrain conditions. An outline for gait adaptation based on this is suggested for future work.

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