405 research outputs found
Fast, Robust, and Versatile Event Detection through HMM Belief State Gradient Measures
Event detection is a critical feature in data-driven systems as it assists
with the identification of nominal and anomalous behavior. Event detection is
increasingly relevant in robotics as robots operate with greater autonomy in
increasingly unstructured environments. In this work, we present an accurate,
robust, fast, and versatile measure for skill and anomaly identification. A
theoretical proof establishes the link between the derivative of the
log-likelihood of the HMM filtered belief state and the latest emission
probabilities. The key insight is the inverse relationship in which gradient
analysis is used for skill and anomaly identification. Our measure showed
better performance across all metrics than related state-of-the art works. The
result is broadly applicable to domains that use HMMs for event detection.Comment: 8 pages, 7 figures, double col, ieee conference forma
Non-Markov Policies to Reduce Sequential Failures in Robot Bin Picking
A new generation of automated bin picking systems using deep learning is
evolving to support increasing demand for e-commerce. To accommodate a wide
variety of products, many automated systems include multiple gripper types
and/or tool changers. However, for some objects, sequential grasp failures are
common: when a computed grasp fails to lift and remove the object, the bin is
often left unchanged; as the sensor input is consistent, the system retries the
same grasp over and over, resulting in a significant reduction in mean
successful picks per hour (MPPH). Based on an empirical study of sequential
failures, we characterize a class of "sequential failure objects" (SFOs) --
objects prone to sequential failures based on a novel taxonomy. We then propose
three non-Markov picking policies that incorporate memory of past failures to
modify subsequent actions. Simulation experiments on SFO models and the EGAD
dataset suggest that the non-Markov policies significantly outperform the
Markov policy in terms of the sequential failure rate and MPPH. In physical
experiments on 50 heaps of 12 SFOs the most effective Non-Markov policy
increased MPPH over the Dex-Net Markov policy by 107%.Comment: 2020 IEEE International Conference on Automation Science and
Engineering (CASE
An online robot collision detection and identification scheme by supervised learning and Bayesian decision theory
This article is dedicated to developing an online collision detection and identification (CDI) scheme for human-collaborative robots. The scheme is composed of a signal classifier and an online diagnosor, which monitors the sensory signals of the robot system, detects the occurrence of a physical human-robot interaction, and identifies its type within a short period. In the beginning, we conduct an experiment to construct a data set that contains the segmented physical interaction signals with ground truth. Then, we develop the signal classifier on the data set with the paradigm of supervised learning. To adapt the classifier to the online application with requirements on response time, an auxiliary online diagnosor is designed using the Bayesian decision theory. The diagnosor provides not only a collision identification result but also a confidence index which represents the reliability of the result. Compared to the previous works, the proposed scheme ensures rapid and accurate CDI even in the early stage of a physical interaction. As a result, safety mechanisms can be triggered before further injuries are caused, which is quite valuable and important toward a safe human-robot collaboration. In the end, the proposed scheme is validated on a robot manipulator and applied to a demonstration task with collision reaction strategies. The experimental results reveal that the collisions are detected and classified within 20 ms with an overall accuracy of 99.6%, which confirms the applicability of the scheme to collaborative robots in practice
Anomaly detection methods in autonomous robotic missions
Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized autonomous robots. This review paper investigates the literature on the detection of anomalies in Autonomous Robotic Missions (ARMs). It reveals different perspectives on anomaly and juxtaposition to fault detection. To reach a consensus, we infer a unified understanding of anomalies that encapsulate their various characteristics observed in ARMs and propose a classification of anomalies in terms of spatial, temporal, and spatiotemporal elements based on their fundamental features. Further, the paper discusses the implications of the proposed unified understanding and classification in ARMs and provides future directions. We envisage a study surrounding the specific use of the term anomaly, and methods for their detection could contribute to and accelerate the research and development of a universal anomaly detection system for ARMs
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