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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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Collisions in Multiple Dimensions: Introduction01:05

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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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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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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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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

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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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Collision Avoidance Mechanism for Swarms of Drones.

Dariusz Marek1,2, Piotr Biernacki2,3, Jakub Szyguła1,2

  • 1Department of Distributed Systems and Informatic Devices, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.

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Summary

This study introduces a simple, low-complexity collision avoidance system for drone swarms. The distributed communication approach ensures safe navigation in dynamic environments, even for small drones.

Keywords:
collision avoidancehardware-in-the-loop (HITL)positioning accuracysoftware-in-the-loop (SITL)swarm of drones

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • Large-scale drone swarms require robust collision avoidance.
  • Dynamic environments pose significant challenges for autonomous navigation.
  • Existing methods may lack scalability or be computationally intensive.

Purpose of the Study:

  • To present a novel, distributed collision avoidance mechanism for drone swarms.
  • To enable autonomous cooperation and safe distance maintenance among drones.
  • To develop a scalable and computationally efficient solution.

Main Methods:

  • Utilizes distributed communication for drones to share positional and trajectory data.
  • Employs repulsion vectors based on proximity to obstacles and other drones.
  • Simulates swarm behavior in complex scenarios with over 20 drones.

Main Results:

  • Demonstrates effective collision avoidance and maintenance of safe distances.
  • Confirms the algorithm's scalability with large drone swarms (up to 25 drones).
  • Shows adaptability to changing environmental conditions and formations.

Conclusions:

  • The proposed mechanism offers a robust and scalable solution for drone swarm coordination.
  • Its simplicity and low computational cost make it suitable for resource-constrained drones.
  • Represents a significant advancement for real-world swarm applications.