Harnessing Vision and Touch for Compliant Robotic Interaction with Soft or Rigid Objects

Creţu, Ana-Maria; Payeur, Pierre

doi:10.1007/978-3-319-55369-6_9

Search citation statements

Order By: Relevance

Paper Sections

Select...

Sensing and Data Processing1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2018

Publication Types

Select...

Article1

Relationship

Self Cite1

Independent0

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data coming from sensors must capture the intrinsic complexity of these systems and in many cases the sensory infrastructure is as sophisticated as them. Consequently, there exists a variety of aspects to consider [4] when designing this process, such as the sensor technologies used, localization and interaction of sensors with the environment, multi-sensor data fusion, and data sampling techniques. In principle, robotic manipulation of non-rigid objects can be mainly categorized with respect to the sensing technologies used, that is, vision, tactile or derived from different modalities.…”

Section: Sensing and Data Processingmentioning

confidence: 99%

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

2018

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Sensing and Data Processingmentioning

confidence: 99%