Bayesian Hybrid Model-State Estimation Applied to Simultaneous Contact                 Formation Recognition and Geometrical Parameter Estimation

Gadeyne, Klaas; Lefebvre, Tine; Bruyninckx, Herman

doi:10.1177/0278364905056196

Cited by 49 publications

(50 citation statements)

References 35 publications

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“…Gadeyne et al (2005) used a particle filter to track a hybrid discrete-continuous probability distribution over a small set of contact states (discrete) and object pose (continuous). The estimator was later scaled to the full set of possible contact states by using a pre-computed contact state graph (Xiao 1993) to generate a sparse transition model between discrete contact state (Meeussen et al 2007).…”

Section: Contact State Estimationmentioning

confidence: 99%

Pose estimation for planar contact manipulation with manifold particle filters

Koval

Pollard

Srinivasa

2015

The International Journal of Robotics Research

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We investigate the problem of using contact sensors to estimate the pose of an object during planar pushing by a fixed-shape hand. Contact sensors are unique because they inherently discriminate between "contact" and "no-contact" configurations. As a result, the set of object configurations that activates a sensor constitutes a lower-dimensional contact manifold in the configuration space of the object. This causes conventional state estimation methods, such as the particle filter, to perform poorly during periods of contact due to particle starvation.In this paper, we introduce the manifold particle filter as a principled way of solving the state estimation problem when the state moves between multiple manifolds of different dimensionality. The manifold particle filter avoids particle starvation during contact by adaptively sampling particles that reside on the contact manifold from the dual proposal distribution. We describe three techniques-one analytical, and two sample-based-of sampling from the dual proposal distribution and compare their relative strengths and weaknesses. We present simulation results that show that all three techniques outperform the conventional particle filter in both speed and accuracy. Additionally, we implement the manifold particle filter on a real robot and show that it successfully tracks the pose of a pushed object using commercially available tactile sensors.

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Section: Contact State Estimationmentioning

confidence: 99%

Pose estimation for planar contact manipulation with manifold particle filters

Koval

Pollard

Srinivasa

2015

The International Journal of Robotics Research

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“…Hence, non-parametric inference schemes relying on a discretization of the probability distribution must be adopted, such as grid-based methods [8,9] or particle filter [11,12]. Unfortunately, the computational complexity of these inference schemes linearly increases with that of the configuration space.…”

Section: Motivationmentioning

confidence: 99%

DOF Decoupling Task Graph Model: Reducing the Complexity of Touch-Based Active Sensing

2015

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This article presents: (i) a formal, generic model for active sensing tasks; (ii) the insight that active sensing actions can very often be searched on less than six-dimensional configuration spaces (bringing an exponential reduction in the computational costs involved in the search); (iii) an algorithm for selecting actions explicitly trading off information gain, execution time and computational cost; and (iv) experimental results of touch-based localization in an industrial setting. Generalizing from prior work, the formal model represents an active sensing task by six primitives: configuration space, information space, object model, action space, inference scheme and action-selection scheme; prior work applications conform to the model as illustrated by four concrete examples. On top of the mentioned primitives, the task graph is then introduced as the relationship to represent an active sensing task as a sequence of low-complexity actions defined over different configuration spaces of the object. The presented act-reason algorithm is an action selection scheme to maximize the expected information gain of each action, explicitly constraining the time allocated to compute and execute the actions. The experimental contributions include localization of objects with: (1) a force-controlled robot equipped with a spherical touch probe; (2) a geometric complexity of the to-be-localized objects up to industrial relevance; (3) an initial uncertainty of (0.4 m, 0.4 m, 2π); and (4) a configuration Robotics 2015, 4 142 of act-reason to constrain the allocated time to compute and execute the next action as a function of the current uncertainty. Localization is accomplished when the probability mass within a 5-mm tolerance reaches a specified threshold of 80%. Four objects are localized with final {mean; standard-deviation} error spanning from {0.0043 m; 0.0034 m} to {0.0073 m; 0.0048 m}.

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“…The Open Robot Control Software (Orocos) [53][54][55][56][57][58][59] is a Real-Time Toolkit (RTT) that helps the developers to build C++ robotics applications. RSCA (Robot Software Communication Architecture) [60], developed in Seoul National University, is a robot middleware for networked home service robots.…”

Section: Robot Softwarementioning

confidence: 99%

Robotics Middleware: A Comprehensive Literature Survey and Attribute-Based Bibliography

Elkady

Sobh

2012

Journal of Robotics

171

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Autonomous robots are complex systems that require the interaction between numerous heterogeneous components (software and hardware). Because of the increase in complexity of robotic applications and the diverse range of hardware, robotic middleware is designed to manage the complexity and heterogeneity of the hardware and applications, promote the integration of new technologies, simplify software design, hide the complexity of low-level communication and the sensor heterogeneity of the sensors, improve software quality, reuse robotic software infrastructure across multiple research efforts, and to reduce production costs. This paper presents a literature survey and attribute-based bibliography of the current state of the art in robotic middleware design. The main aim of the survey is to assist robotic middleware researchers in evaluating the strengths and weaknesses of current approaches and their appropriateness for their applications. Furthermore, we provide a comprehensive set of appropriate bibliographic references that are classified based on middleware attributes.

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Bayesian Hybrid Model-State Estimation Applied to Simultaneous Contact Formation Recognition and Geometrical Parameter Estimation

Cited by 49 publications

References 35 publications

Pose estimation for planar contact manipulation with manifold particle filters

Pose estimation for planar contact manipulation with manifold particle filters

DOF Decoupling Task Graph Model: Reducing the Complexity of Touch-Based Active Sensing

Robotics Middleware: A Comprehensive Literature Survey and Attribute-Based Bibliography

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