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Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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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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Reinforcement01:23

Reinforcement

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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

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Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
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Masking and Demasking Agents01:19

Masking and Demasking Agents

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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
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Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

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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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Related Experiment Video

Updated: Dec 23, 2025

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

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Counter a Drone in a Complex Neighborhood Area by Deep Reinforcement Learning.

Ender Çetin1, Cristina Barrado2, Enric Pastor2

  • 1Aerospace Science and Technology, UPC BarcelonaTECH, 08860 Castelldefels, Spain.

Sensors (Basel, Switzerland)
|April 25, 2020
PubMed
Summary

Artificial intelligence (AI) enhances counter-drone technology for efficient aerial threat neutralization. Deep reinforcement learning enables autonomous drones to intercept targets while avoiding obstacles, improving safety and security.

Keywords:
UAVcounter dronesdeep reinforcement learningdouble deep Q-network (DDQN)joint neural network (JNN)transfer learning

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Area of Science:

  • Robotics and Autonomous Systems
  • Artificial Intelligence
  • Aerospace Engineering

Background:

  • Counter-drone technology is rapidly advancing with artificial intelligence (AI).
  • AI-powered systems offer enhanced accuracy and efficiency in drone threat mitigation compared to traditional methods.
  • Autonomous systems are crucial for real-time threat detection and engagement.

Purpose of the Study:

  • To propose a deep reinforcement learning (DRL) architecture for an autonomous counter-drone system.
  • To train a learning drone to detect and intercept a target drone within a complex suburban environment.
  • To evaluate the effectiveness of transfer learning in improving DRL agent performance and reducing training crashes.

Main Methods:

  • A DRL architecture was developed for a learning drone equipped with a front camera for depth image capture.
  • The state space included depth images and scalar parameters (velocities, distances, angles).
  • Transfer learning was applied using pre-trained model weights to accelerate training and improve performance.

Main Results:

  • The DRL agent successfully learned to detect and avoid both stationary and moving obstacles.
  • Transfer learning demonstrated a significant improvement in initial training rewards (approx. 35 more).
  • Transfer learning reduced training crashes by 65% when ground obstacles were included.

Conclusions:

  • AI-driven counter-drone technology, particularly using DRL, offers a promising solution for enhanced aerial security.
  • Autonomous drone systems can effectively neutralize threats while navigating complex environments.
  • Transfer learning is a valuable technique for optimizing the training of autonomous counter-drone agents.