Towards Assessing Compliant Robotic Grasping From First-Object Perspective via Instrumented Objects

Grasping compliant objects is difficult for robots - applying too little force may cause the grasp to fail, while too much force may lead to object damage. A robot needs to apply the right amount of force to quickly and confidently grasp the objects so that it can perform the required task. Although...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE robotics and automation letters Ročník 9; číslo 7; s. 6320 - 6327
Hlavní autori: Knopke, Maceon, Zhu, Liguo, Corke, Peter, Zhang, Fangyi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Actuators
Damage
Estimation
Force
Force measurement
Grasping
Grasping (robotics)
Hall effect
Magnetic sensors
Magnets
methods and tools for robot system design
Neural networks
Performance assessment
Robot sensing systems
Robots
Sensors
soft sensors and actuators
System analysis and design
Three-dimensional displays
ISSN:2377-3766, 2377-3766
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Grasping compliant objects is difficult for robots - applying too little force may cause the grasp to fail, while too much force may lead to object damage. A robot needs to apply the right amount of force to quickly and confidently grasp the objects so that it can perform the required task. Although some methods have been proposed to tackle this issue, performance assessment is still a problem for directly measuring object property changes and possible damage. To fill the gap, a new concept is introduced in this paper to assess compliant robotic grasping using instrumented objects. A proof-of-concept design is proposed to measure the force applied on a soft cuboid object from a first-object perspective. The design can detect multiple contact locations and applied forces on its surface by using multiple embedded 3D Hall sensors to detect deformation relative to embedded magnets. The contact estimation is achieved by interpreting the Hall-effect signals using neural networks. In comprehensive experiments, the design achieved good performance in estimating contacts from each single face of the cuboid and decent performance in detecting contacts from multiple faces when being used to evaluate grasping from a parallel jaw gripper, demonstrating the effectiveness of the design and the feasibility of the concept.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2024.3405371