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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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Types of Global Positioning System Surveys

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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Decision Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
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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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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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多无人机协作搜索和攻击任务决策在未知的环境.

Zibin Liang1,2,3, Qing Li1,2,3, Guodong Fu1,2,3

  • 1Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing 100192, China.

Sensors (Basel, Switzerland)
|September 9, 2023
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概括

本研究介绍了用于协调任务的多无人机分布式自组织合作情报监视和作战 (CISCS) 战略. 新方法提高了无人机的形成,任务分配和避开障碍,以提高侦察和战斗效率.

关键词:
这就是Q-learning.殖民地算法 殖民地算法人工潜力领域的人工潜力领域协调的行动是协调的行动.多个无人机的UAV.

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科学领域:

  • 机器人和控制系统 机器人和控制系统
  • 人工智能的人工智能
  • 分布式系统 分布式系统

背景情况:

  • 在侦察和战斗中协调的多无人机 (UAV) 操作面临分布式控制,通信和阵营稳定性方面的挑战.
  • 集中控制系统在动态的多无人机场景中扎着通信限制和可扩展性.
  • 有效的任务分配和实时避开障碍对于成功的自主多无人机任务至关重要.

研究的目的:

  • 提出一个新的多无人机分布式自组织合作情报监视和战斗 (CISCS) 战略.
  • 解决多无人机系统中集中控制,通信,阵列稳定性和任务分配方面的局限性.
  • 实现自主决策,提高协调无人机侦察和作战行动的效率.

主要方法:

  • 开发分布式控制战略,以克服集中控制和通信问题.
  • 为快速和稳定的多无人机阵列实施时间受限的阵列控制器.
  • 设计一个多任务分配算法,使用基于Q-Learning的改进的分布式群优化 (ACO),结合扩展搜索和预防过早融合的策略.
  • 集成无人机避障模块,考虑内部碰撞并提供实时避障路径.

主要成果:

  • 一个有限时间形成算法使得在3D空间中多无人机的快速形成成为可能.
  • 基于Q-Learning的改进分布式ACO算法有效地分配任务并生成序列,使用策略来增强搜索范围并避免过早的融合.
  • 避障模块提供实时路径,防止无人机之间的内部碰撞.
  • 模拟证明了拟议的CISCS战略的可行性和可靠性.

结论:

  • 拟议的多无人机分布式自组织合作情报监视和作战 (CISCS) 战略有效地解决了协调多无人机作战的关键挑战.
  • 分布式方法,有限时间形成控制和增强的任务分配算法显著提高了任务效率和自主性.
  • 该战略的组成部分协同工作,以确保在复杂的侦察和战斗场景中提供强大的性能,并通过模拟进行验证.