42,341 research outputs found
Fast Graph-Based Object Segmentation for RGB-D Images
Object segmentation is an important capability for robotic systems, in
particular for grasping. We present a graph- based approach for the
segmentation of simple objects from RGB-D images. We are interested in
segmenting objects with large variety in appearance, from lack of texture to
strong textures, for the task of robotic grasping. The algorithm does not rely
on image features or machine learning. We propose a modified Canny edge
detector for extracting robust edges by using depth information and two simple
cost functions for combining color and depth cues. The cost functions are used
to build an undirected graph, which is partitioned using the concept of
internal and external differences between graph regions. The partitioning is
fast with O(NlogN) complexity. We also discuss ways to deal with missing depth
information. We test the approach on different publicly available RGB-D object
datasets, such as the Rutgers APC RGB-D dataset and the RGB-D Object Dataset,
and compare the results with other existing methods
The hydra hand: a mode-switching underactuated gripper with precision and power grasping modes
Human hands are able to grasp a wide range of object sizes, shapes, and weights, achieved via reshaping and altering their apparent grasping stiffness between compliant power and rigid precision. Achieving similar versatility in robotic hands remains a challenge, which has often been addressed by adding extra controllable degrees of freedom, tactile sensors, or specialised extra grasping hardware, at the cost of control complexity and robustness. We introduce a novel reconfigurable four-fingered two-actuator underactuated gripper—the Hydra Hand—that switches between compliant power and rigid precision grasps using a single motor, while generating grasps via a single hydraulic actuator—exhibiting adaptive grasping between finger pairs, enabling the power grasping of two objects simultaneously. The mode switching mechanism and the hand's kinematics are presented and analysed, and performance is tested on two grasping benchmarks: one focused on rigid objects, and the other on items of clothing. The Hydra Hand is shown to excel at grasping large and irregular objects, and small objects with its respective compliant power and rigid precision configurations. The hand's versatility is then showcased by executing the challenging manipulation task of safely grasping and placing a bunch of grapes, and then plucking a single grape from the bunch
Sensing and Control for Robust Grasping with Simple Hardware
Robots can move, see, and navigate in the real world outside carefully structured factories, but they cannot yet grasp and manipulate objects without human intervention. Two key barriers are the complexity of current approaches, which require complicated hardware or precise perception to function effectively, and the challenge of understanding system performance in a tractable manner given the wide range of factors that impact successful grasping. This thesis presents sensors and simple control algorithms that relax the requirements on robot hardware, and a framework to understand the capabilities and limitations of grasping systems.Engineering and Applied Science
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