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相关概念视频

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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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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Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

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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

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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Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

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When analyzing one-dimensional motion with constant acceleration, the problem-solving strategy involves identifying the known quantities and choosing the appropriate kinematic equations to solve for the unknowns. Either one or two kinematic equations are needed to solve for the unknowns, depending on the known and unknown quantities. Generally, the number of equations required is the same as the number of unknown quantities in the given example. Two-body pursuit problems always require two...
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Principle of Linear Impulse and Momentum for a Single Particle: Problem Solving01:23

Principle of Linear Impulse and Momentum for a Single Particle: Problem Solving

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Consider a wooden box and a cylinder of known masses m1 and m2, respectively,  hanging from a ceiling with the help of a massless pulley system.
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相关实验视频

Updated: Jun 5, 2025

Operation of the Collaborative Composite Manufacturing CCM System
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Operation of the Collaborative Composite Manufacturing CCM System

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模型预测路径积分用于分散的多代理避免碰撞.

Stepan Dergachev1,2, Konstantin Yakovlev1,2

  • 1HSE University, Moscow, Russia.

PeerJ. Computer science
|December 16, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种针对多代理系统的通用防撞方法,增强了模型预测路径集成 (MPPI) 控制与最佳相互防撞 (ORCA) 约束. 这种方法可以确保各种机器人动力学的安全性.

关键词:
避免碰撞,避免碰撞.分散的多代理导航系统.分散的多代理系统 分散的多代理系统动力学限制 动力学限制模型预测路径积分的模型.多机器人系统多机器人系统以采样为基础的优化

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A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
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MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
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A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能
  • 控制系统 控制系统

背景情况:

  • 分散的多代理导航需要有效避免碰撞.
  • 现有的方法往往忽略动力学约束或是特定于模型.

研究的目的:

  • 为任意的亲缘动力运动模型开发一个通用的去中心化避免碰撞的方法.
  • 为多代理场景增强模型预测路径集成 (MPPI) 算法.

主要方法:

  • 将最佳相互碰撞避免 (ORCA) 约束集成到MPPI中.
  • 通过凸优化推导安全分布.
  • 理论上的安全保证和经验上的评估.

主要成果:

  • 提出的方法成功地处理了任意的亲缘动力学模型.
  • 在各种模拟设置中超越最先进的方法.
  • 解决现有方法难以解决的问题.

结论:

  • 一般化方法为各种多代理系统提供了强大而安全的避免碰撞.
  • 通过ORCA约束增强的MPPI在分散的导航中取得了重大进展.