2,549 research outputs found
Realtime hybrid task-based control for robots and machine tools
Abstract-This paper presents work in the field of hard realtime robotics and machine control. We analyse the requirements of a hybrid realtime control task specification allowing the integration of discrete and continuous control tasks. We propose an application independent task structure providing data flow consistency under simulataneous access by different control layers. We provide an execution flow mechanism to guarantee execution time determinism, yet allowing flexibility to react to a changing environment. We use state machines for process monitoring and a thread-safe realtime event system to communicate changes. The tasks can be distributed over a network and communicate using interfaces or manipulate streams of data in the loop. The presented task structure is applied to a real world example
Rational physical agent reasoning beyond logic
The paper addresses the problem of defining a theoretical physical agent framework that satisfies practical requirements of programmability by non-programmer engineers and at the same time permitting fast realtime operation of agents on digital computer networks. The objective of the new framework is to enable the satisfaction of performance requirements on autonomous vehicles and robots in space exploration, deep underwater exploration, defense reconnaissance, automated manufacturing and household automation
Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models
The gap between our ability to collect interesting data and our ability to
analyze these data is growing at an unprecedented rate. Recent algorithmic
attempts to fill this gap have employed unsupervised tools to discover
structure in data. Some of the most successful approaches have used
probabilistic models to uncover latent thematic structure in discrete data.
Despite the success of these models on textual data, they have not generalized
as well to image data, in part because of the spatial and temporal structure
that may exist in an image stream.
We introduce a novel unsupervised machine learning framework that
incorporates the ability of convolutional autoencoders to discover features
from images that directly encode spatial information, within a Bayesian
nonparametric topic model that discovers meaningful latent patterns within
discrete data. By using this hybrid framework, we overcome the fundamental
dependency of traditional topic models on rigidly hand-coded data
representations, while simultaneously encoding spatial dependency in our topics
without adding model complexity. We apply this model to the motivating
application of high-level scene understanding and mission summarization for
exploratory marine robots. Our experiments on a seafloor dataset collected by a
marine robot show that the proposed hybrid framework outperforms current
state-of-the-art approaches on the task of unsupervised seafloor terrain
characterization.Comment: 8 page
Responsible Autonomy
As intelligent systems are increasingly making decisions that directly affect
society, perhaps the most important upcoming research direction in AI is to
rethink the ethical implications of their actions. Means are needed to
integrate moral, societal and legal values with technological developments in
AI, both during the design process as well as part of the deliberation
algorithms employed by these systems. In this paper, we describe leading ethics
theories and propose alternative ways to ensure ethical behavior by artificial
systems. Given that ethics are dependent on the socio-cultural context and are
often only implicit in deliberation processes, methodologies are needed to
elicit the values held by designers and stakeholders, and to make these
explicit leading to better understanding and trust on artificial autonomous
systems.Comment: IJCAI2017 (International Joint Conference on Artificial Intelligence
A Multi-Level Control Architecture for the Bionic Handling Assistant
Rolf M, Neumann K, QueiĂer J, Reinhart F, Nordmann A, Steil JJ. A Multi-Level Control Architecture for the Bionic Handling Assistant. Advanced Robotics. 2015;29(13: SI):847-859.The Bionic Handling Assistant is one of the largest soft continuum robots and very special in be-
ing a pneumatically operated platform that is able to bend, stretch, and grasp in all directions. It
nevertheless shares many challenges with smaller continuum and other softs robots such as parallel
actuation, complex movement dynamics, slow pneumatic actuation, non-stationary behavior, and a
lack of analytic models. To master the control of this challenging robot, we argue for a tight inte-
gration of standard analytic tools, simulation, control, and state of the art machine learning into an
overall architecture that can serve as blueprint for control design also beyond the BHA. To this aim,
we show how to integrate specific modes of operation and different levels of control in a synergistic
manner, which is enabled by using modern paradigms of software architecture and middleware. We
thereby achieve an architecture with unique overall control abilities for a soft continuum robot that
allow for exible experimentation towards compliant user-interaction, grasping, and online learning of
internal models
A Survey of Applications and Human Motion Recognition with Microsoft Kinect
Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this paper, we present, a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classification of motion recognition techniques to highlight the different approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lower-level computer vision tasks such as segmentation, object detection and human pose estimation
Validation of real-time properties of a robotic software architecture
National audienceIn this paper, we propose a mechanism allowing to evaluate the schedulability of a robotic software architecture, and then validate its real-time properties. The robotic software architecture is described through a Domain Specific Language (DSL), MAUVE, that allows to model communicating components. The evaluation of schedulability of the architecture consists in first computing the Worst-Case Execution Time (WCET) of the elementary functions of the components. Then the Worst Case Response Time (WCRT) of the component is computed from the elementary WCET and the component models, allowing to validate the schedulatiblity of the architecture. We illustrate our methodology on the evaluation of a control architecture for a ground mobile robot
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