Object-Independent Grasping in Heavy Clutter
Source abstract
Abstract
When grasping objects in a cluttered environment, a key challenge is to find appropriate poses to grasp effectively. Accordingly, several grasping algorithms based on artificial neural networks have been developed recently. However, these methods require large amounts of data for learning and high computational costs. Therefore, we propose a depth difference image-based bin-picking (DBP) algorithm that does not use a neural network. DBP predicts the grasp pose from the object and its surroundings, which are obtained through depth filtering and clustering. The object region is estimated by the density-based spatial clustering of applications with noise (DBSCAN) algorithm, and a depth difference image (DDI) that represents the depth difference between adjacent areas is defined. To validate the performance of the DBP scheme, bin-picking experiments were conducted on 45 different objects, along with bin-picking experiments in heavy clutters. DBP exhibited success rates of 78.6% and 83.3%, respectively. In addition, DBP required a computational time of approximately 1.4 s for each attempt.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robot Manipulation and Learning · Soft Robotics and Applications · Hand Gesture Recognition Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, Korea University, Seoul 136-713, Korea
Funding
Korea Government Ministry of Science and ICT
References (20)
Show source-provided references
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