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Related Concept Videos

Kinetic Friction01:26

Kinetic Friction

1.0K
Consider a truck trying to pull a stationary car. As the truck exerts a force on the car, static friction is created at the point of contact between the two surfaces. This frictional force resists the car's movement and keeps it at rest. However, when the applied force by the truck surpasses the limiting static frictional force, an interesting phenomenon occurs. The frictional force at the interface reduces to a lower value, known as the kinetic frictional force. At this point, the car...
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Static and Kinetic Frictional Force01:05

Static and Kinetic Frictional Force

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One of the simpler characteristics of sliding friction is that it is parallel to the contact surfaces between systems, and is always in a direction that opposes the motion or attempted motion of the systems relative to each other. If two systems are in contact and moving relative to one another, then the friction between them is called kinetic friction. For example, kinetic friction slows a hockey puck sliding on ice.
However, if two systems are in contact and are stationary relative to one...
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Related Experiment Video

Updated: Sep 3, 2025

Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs
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Learning-Based Slip Detection for Robotic Fruit Grasping and Manipulation under Leaf Interference.

Hongyu Zhou1, Jinhui Xiao1, Hanwen Kang1

  • 1Laboratory of Motion Generation and Analysis, Faculty of Engineering, Monash University, Clayton, VIC 3800, Australia.

Sensors (Basel, Switzerland)
|July 28, 2022
PubMed
Summary
This summary is machine-generated.

Robotic harvesting struggles with leaf interference. This study introduces a learning-based method to detect and handle grasp slips caused by leaves, improving robotic harvesting success rates.

Keywords:
leaf interferencelong-short-term memory (LSTM)robotic harvestingslip detection

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Area of Science:

  • Robotics
  • Agricultural Technology
  • Computer Vision

Background:

  • Robotic harvesting has advanced significantly, yet obstacle handling, particularly leaf interference, limits success.
  • Leaf interference during grasping causes object slip, reducing harvest efficiency and performance.

Purpose of the Study:

  • To address leaf interference in robotic harvesting by developing a method for slip detection and handling.
  • To identify and analyze the link between leaf interference, object slip, and suboptimal harvesting outcomes.

Main Methods:

  • Analysis of fruit grasping motion and force under leaf interference conditions.
  • Development of a learning-based perception and manipulation algorithm for slip detection.
  • Implementation of timely robotic reactions to detected grasp slips.

Main Results:

  • The proposed algorithm accurately detects grasp slips caused by leaf interference with 94% accuracy.
  • Demonstrated the connection between leaf interference, object slip, and reduced harvest performance.
  • Validated the effectiveness of sensing-based manipulation for robotic harvesting.

Conclusions:

  • The developed learning-based approach effectively detects and handles grasp slips due to leaf interference.
  • This sensing-based manipulation method shows significant potential for enhancing robotic fruit harvesting.
  • The approach is adaptable for broader applications in pick-and-place robotics.