Mr. Kevin Carey Profile

Kevin Carey

at Univ of Maryland College Park

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Proceedings Article | 13 June 2023 Presentation + Paper

Using metareasoning to improve autonomous robot planning

Jeffrey Herrmann, Kevin Carey, Sidney Molnar

Proceedings Volume 12544, 125440V (2023) https://doi.org/10.1117/12.2662961

KEYWORDS: Unmanned aerial vehicles, Robotics, Robotic systems, Ground robots

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An autonomous robot’s onboard computational resources are limited due to size, weight, and power limits. Thus, optimizing the use of those resources is essential so that the robot can complete its mission reliably and efficiently. Metareasoning, a branch of artificial intelligence, enables a robot to monitor and control its perception, mapping, planning, and other reasoning processes in response to changes in the robot and its environment. Metareasoning is implemented in a meta-level that is logically separate from the object level (which performs the reasoning processes). Previous work has developed metareasoning approaches for specific robotic systems, but these are not easy to generalize. This paper describes our implementation of a metareasoning approach in the Army Research Laboratory (ARL) ground autonomy stack, which is deployable on a variety of robotic platforms. This paper describes the general approach and our implementation of a metareasoning node that can switch the global and local path planners when a planning failure occurs. The results of simulated experiments show that adding metareasoning increases the likelihood of mission success in some cases. More research is needed to optimize the metareasoning approach. Ultimately comprehensive metareasoning that can control the most important aspects of object level reasoning will enable an autonomous robot to deploy its limited computational resources more effectively and complete its mission more reliably.

Proceedings Article | 21 April 2020 Presentation + Paper

Comparison of skeleton models and classification accuracy for posture-based threat assessment using deep-learning

Kevin Carey, Benjamin Abruzzo, Christopher Lowrance, Eric Sturzinger, Ross Arnold, Christopher Korpela

Proceedings Volume 11413, 1141321 (2020) https://doi.org/10.1117/12.2556422

KEYWORDS: Firearms, Neural networks, Weapons, Image classification, Video

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This paper compares the effectiveness of two different skeletal pose models for a near real-time, multi-stage classifier. A cascaded neural-network (NN) classifier was previously developed to identify the level of threat posed by an armed person based on detected weapons and body posture. On an updated database of images containing armed individuals and groups, AlphaPose was used to calculate both MPII and COCO skeletons while OpenPose was used to calculate the COCO only. For comparison, we evaluated the importance of individual skeletal joints by systematically removing specific joints from the feature vector and retraining a reduced order network. On the database of images, the AlphaPose-COCO network was best able to correctly classify the threat presented by individuals, 83.7% on average, while AlphaPose-MPII registered 82.2% and 77.6% for OpenPose-COCO. As expected, the most important single joint in both skeleton models is the location of the pistol. As a guide for others deciding which skeleton to use for further studies, we conclude that neither skeleton significantly outperforms the other.

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