Concept for a simulation-based approach towards automated handling of deformable objects — A bin picking scenario

Wnuk, Markus; Pott, Andreas; Wang, Xu; Lechler, Armin; Verl, Alexander

doi:10.1109/m2vip.2017.8211452

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…The goal is to achieve efficient path planning for the cleaning arm as well as for the grasping arm, as regrasps might be necessary in order to hold the part closer to the area being cleaned and avoid excessive deformation. Wnuk et al [113] performed a general analysis of a complete bin-picking scenario where the system could handle non-rigid objects through simulation. They described the different steps of the process, namely object localization, approach, grasping, and subsequent manipulation, as well as the different challenges faced during each step.…”

Section: Other Control Strategiesmentioning

confidence: 99%

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

2018

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This paper aims to provide a comprehensive survey of recent advancements in modelling and autonomous manipulation of non-rigid objects. It first summarizes the recent advances in sensing and modelling of such objects with a focus on describing the methods and technologies used to measure their shape and estimate their material and physical properties. Formal representations considered to predict the deformation resulting from manipulation of non-rigid objects are then investigated. The third part provides a survey of planning and control strategies exploited to operate dexterous robotic systems while performing various tasks on objects made of different non-rigid materials.

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Section: Other Control Strategiesmentioning

confidence: 99%

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

2018

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“…The multi-view geometry can be adapted in order to extract the object's geometric features [53], while the epipolar geometry can be used to generate the transformation matrix [54]. Some other approaches suggest visuals-based tracking like [55], which performs tracking approach based on CAD model for micro-assembly and like [56] which employs 6D posture estimation for end-effector tracking in a scanning electron microscope to enable higher-quality automated processes and accurate measurements.…”

Section: D-data-based Techniquesmentioning

confidence: 99%

MonoGhost: Lightweight Monocular GhostNet 3D Object Properties Estimation for Autonomous Driving

El-Dawy,

El-Zawawi,

El-Habrouk

2023

Robotics

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Effective environmental perception is critical for autonomous driving; thus, the perception system requires collecting 3D information of the surrounding objects, such as their dimensions, locations, and orientation in space. Recently, deep learning has been widely used in perception systems that convert image features from a camera into semantic information. This paper presents the MonoGhost network, a lightweight Monocular GhostNet deep learning technique for full 3D object properties estimation from a single frame monocular image. Unlike other techniques, the proposed MonoGhost network first estimates relatively reliable 3D object properties depending on efficient feature extractor. The proposed MonoGhost network estimates the orientation of the 3D object as well as the 3D dimensions of that object, resulting in reasonably small errors in the dimensions estimations versus other networks. These estimations, combined with the translation projection constraints imposed by the 2D detection coordinates, allow for the prediction of a robust and dependable Bird’s Eye View bounding box. The experimental outcomes prove that the proposed MonoGhost network performs better than other state-of-the-art networks in the Bird’s Eye View of the KITTI dataset benchmark by scoring 16.73% on the moderate class and 15.01% on the hard class while preserving real-time requirements.

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