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Related Concept Videos

Types of Collisions - II01:19

Types of Collisions - II

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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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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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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RRT-CS: A free-collision planner for capsule-like SCORBOT by iterated learning.

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  • 1HUTECH Institute of Engineering, HUTECH University, Ho Chi Minh, Vietnam.

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This study introduces an enhanced Rapidly-exploring Random Trees (RRT) algorithm with visual servoing for robot navigation in unknown environments. The efficient approach demonstrates suitability for real-world robotic applications.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Robotic navigation in unknown environments presents significant challenges.
  • Accurate environment recognition and path planning are crucial for autonomous systems.

Purpose of the Study:

  • To enhance the Rapidly-exploring Random Trees (RRT) algorithm with visual servoing for improved environment recognition.
  • To evaluate the algorithm's effectiveness and robustness on a SCORBOT-ER-VII robotic platform.

Main Methods:

  • Integration of an enhanced RRT algorithm with a visual servoing technique.
  • Utilizing the SCORBOT-ER-VII robotic platform with multiple links and servo motors.
  • Testing across diverse scenarios, from obstacle-free to complex configurations, via simulations and hardware experiments.

Main Results:

  • Planning time correlates with environmental complexity.
  • Trajectory smoothing accounts for <10% of processing time.
  • RRT-based profile generation constitutes the majority (2/3) of computational effort.

Conclusions:

  • The enhanced RRT algorithm with visual servoing is computationally efficient.
  • The proposed method is robust and suitable for real-world robotic navigation tasks.
  • The approach effectively handles environment recognition and path planning challenges.