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Related Concept Videos

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.6K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

14.7K
Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
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Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

5.8K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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Elastic Collisions: Introduction01:00

Elastic Collisions: Introduction

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An elastic collision is one that conserves both internal kinetic energy and momentum. Internal kinetic energy is the sum of the kinetic energies of the objects in a system. Truly elastic collisions can only be achieved with subatomic particles, such as electrons striking nuclei. Macroscopic collisions can be very nearly, but not quite, elastic, as some kinetic energy is always converted into other forms of energy such as heat transfer due to friction and sound. An example of a nearly...
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Types of Collisions - II01:19

Types of Collisions - II

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When two or more objects collide with each other, they can stick together to form one single composite object (after collision). The total mass of the object after the collision is the sum of the masses of the original objects, and it moves with a velocity dictated by the conservation of momentum. Although the system's total momentum remains constant, the kinetic energy decreases, and thus such a collision is an inelastic collision. Most of the collisions between objects in daily life are...
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Types Of Collisions - I01:04

Types Of Collisions - I

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When two objects come in direct contact with each other, it is called a collision. During a collision, two or more objects exert forces on each other in a relatively short amount of time. A collision can be categorized as either an elastic or inelastic collision. If two or more objects approach each other, collide and then bounce off, moving away from each other with the same relative speed at which they approached each other, the total kinetic energy of the system is said to be conserved. This...
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Related Experiment Video

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Operation of the Collaborative Composite Manufacturing CCM System
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Uncertainty-Aware Knowledge Distillation for Collision Identification of Collaborative Robots.

Wookyong Kwon1, Yongsik Jin1, Sang Jun Lee2

  • 1Daegu-Gyeongbuk Research Center, Electronics and Telecommunications Research Institute (ETRI), Daegu 42994, Korea.

Sensors (Basel, Switzerland)
|October 13, 2021
PubMed
Summary

This study introduces a deep learning method for identifying external collisions in collaborative robots. Uncertainty-aware knowledge distillation enhances accuracy, improving safety in human-robot interaction.

Keywords:
collaborative robotcollision identificationdeep learningknowledge distillationuncertainty estimation

Related Experiment Videos

Last Updated: Oct 17, 2025

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Collaborative robots are increasingly used in industrial settings.
  • Effective human-robot interaction necessitates robust collision identification systems.
  • Preventing accidents is crucial for safe collaborative robot deployment.

Purpose of the Study:

  • To propose a deep learning method for identifying external collisions in 6-DoF articulated robots.
  • To enhance the accuracy of collision identification by addressing network uncertainties.
  • To improve the safety and reliability of human-robot collaboration.

Main Methods:

  • A deep learning approach based on CollisionNet is extended to pinpoint external force locations.
  • Uncertainty-aware knowledge distillation is employed to train a student network.
  • Sample-level uncertainties are quantified using a teacher network, guiding student network training with focused penalties.

Main Results:

  • The proposed method effectively identifies external collisions in articulated robots.
  • Uncertainty-aware knowledge distillation significantly improves the accuracy of the deep neural network.
  • Experimental results validate the enhanced performance in collision identification.

Conclusions:

  • The developed deep learning method offers improved external collision identification for collaborative robots.
  • Uncertainty-aware knowledge distillation is a key technique for boosting the performance of collision detection networks.
  • This advancement contributes to safer and more reliable human-robot interaction in industrial applications.