Springer Handbook of Robotics

By Bruno Siciliano. 2008

2,228 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

(CAD)$^2$RL: Real Single-Image Flight without a Single Real Image (2016, methodology): “Standard methods for collision-free indoor navigation take a two step approach to the problem: ﬁrst map out the local environment and determine its geometry, and then compute a collision-free path for reaching the destination [29].”
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection (2016, background): “Techniques such as visual servoing (Siciliano & Khatib, 2007) perform continuous feedback on visual features, but typically require the features to be speciﬁed by hand, and both open loop perception and feedback (e.g. via visual servoing) requires manual or automatic calibration to determine the…”
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping (2018, background): “ systems might be strongly inﬂuenced by the amount of data available [6]. A natural avenue to overcome these data requirements is to look back at the success of analytic, model-based grasping methods [1], which incorporate our prior knowledge of physics and geometry. We can incorporate this prior kn”
Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics (2017, methodology): “An alternative approach is to plan grasps using physicsbased analyses such as caging [46], grasp wrench space (GWS) analysis [44], robust GWS analysis [56], or simulation [25], which can be rapidly computed using Cloud Computing [27].”
Tactile sensing in dexterous robot hands - Review (2015, neutral): “During the last decades, industrial robots have replaced humans in heavy, repetitive or/and unsafe manufacturing tasks [3].”
QP-based refined manipulability-maximizing scheme for coordinated motion planning and control of physically constrained wheeled mobile redundant manipulators (2016, methodology): “According to some researches on the kinematics of (fixed-base) robot manipulators [9,12], the specifical kinematics equation of the six-DOF manipulator shown in Fig.”
Scale Invariant Feature Transform (2012, background): “…to a set of known references, (ii) mapping the surrounding from image data that are acquired as the robot moves around (See et al. 2005, Saeedi et al. 2006) or (iii) recognizing and establishing geometric relations to objects in the environment for robot manipulation (Siciliano and Khatib 2008).”
Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection (2016, methodology): “Another related area is visual servoing, which addresses moving a camera or end-effector to a desired pose using visual feedback [Kragic and Christensen, 2002, Siciliano and Khatib, 2007].”
Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018, background): “review of control related aspects of HRC and approaches to sensing can be found in [19].”
Model-Less Feedback Control of Continuum Manipulators in Constrained Environments (2014, background): “Task-space closed-loop controllers for discrete manipulators1 [12] often use a kinematic model of the robot Jacobian that maps actuator velocities to the end-effector velocity [11].”
Learning a visuomotor controller for real world robotic grasping using simulated depth images (2017, methodology): “predicted good grasp poses (3.3). Visual servoing methods use visual feedback to move a camera to a target pose that depends directly on the object pose. While there are numerous methods in this area [10], only a small amount of previous work addresses using visual feedback directly for grasping [11”