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Related Concept Videos

Elastic Collisions: Case Study01:15

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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: Problem Solving01:06

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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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Types of Collisions - II01:19

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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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Elastic Collisions: Introduction01:00

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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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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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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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Using collision cones to assess biological deconfliction methods.

Natalie L Brace1, Tyson L Hedrick2, Diane H Theriault3

  • 1William E. Boeing Department of Aeronautics and Astronautics, University of Washington, Seattle, WA, USA nbrace@uw.edu.

Journal of the Royal Society, Interface
|September 23, 2016
PubMed
Summary

Biological systems excel at collision avoidance. Researchers applied a collision avoidance algorithm to bat, swallow, and fish trajectory data, finding good agreement with observed movements for potential engineering improvements.

Keywords:
animal behaviourcollision avoidance algorithmcollision conesmulti-species comparisonnonlinear controlvelocity obstacles

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Area of Science:

  • Robotics and biomechanics
  • Comparative animal behavior and locomotion

Background:

  • Engineered autonomous systems lag behind biological systems in reactive collision avoidance.
  • Understanding biological strategies can inform the development of advanced autonomous systems.

Purpose of the Study:

  • To apply a collision avoidance algorithm to biological trajectory data.
  • To compare algorithmic predictions with observed animal movements.
  • To identify potential improvements for engineered collision avoidance systems.

Main Methods:

  • Digitized video trajectory data from bats, swallows, and fish were analyzed.
  • A collision avoidance algorithm utilized visual cues (relative position and velocity).
  • Obstacle sets were defined by animal sensing ranges (metric and topological distance).

Main Results:

  • The algorithm defined collision cones to determine safe velocities with minimal deviation.
  • Algorithmic velocities showed strong agreement with observed biological velocities across species.
  • The study validates the algorithm as a comparative basis for biological movement.

Conclusions:

  • Biological collision avoidance strategies can be effectively modeled using algorithmic approaches.
  • The findings suggest pathways for enhancing the performance of engineered autonomous systems.
  • Further research can refine algorithms based on biological insights for improved safety and efficiency.