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相关概念视频

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.3K
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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Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
695
Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

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Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
619
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
676
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
424
Machines: Problem Solving II01:30

Machines: Problem Solving II

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
336

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Cooperative Object Transportation Using Curriculum-Based Deep Reinforcement Learning.

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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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基于深度强化学习的物体运输使用任务空间分解.

Gyuho Eoh1

  • 1Department of Mechatronics Engineering, Tech University of Korea, 237 Sangidaehak-ro, Siheung-si 15073, Gyeonggi-do, Republic of Korea.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
概括

本研究引入了一种新的深度强化学习 (DRL) 方法,使用任务空间分解 (TSD) 进行机器人物体运输. 这种方法使机器人能够在不需要重新学习的情况下在复杂的环境中导航,克服了以前的DRL方法的局限性.

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
  • 机器学习 机器学习

背景情况:

  • 传统的对象运输深度强化学习 (DRL) 仅限于特定的小环境.
  • 由于对环境的依赖,DRL方法在复杂或大规模的环境中往往难以趋同.
  • 现有的方法需要广泛的再培训,以适应新的或不同的环境.

研究的目的:

  • 提出一种基于DRL的新型物体运输方法,克服先前方法的局限性.
  • 提高DRL在机器人操纵任务中的适应性和可扩展性.
  • 使机器人能够在大而复杂的环境中运输物体,而无需重新学习.

主要方法:

  • 该研究使用任务空间分解 (TSD) 来将复杂的运输任务分解为更简单的子任务.
  • 机器人首先在具有简单结构的标准学习环境 (SLE) 中学习对象运输.
  • 任务空间被分解成具有定义子目标的子任务空间,机器人可以顺序实现这些目标.

主要成果:

  • 提出的方法证明了在多样化和复杂的模拟环境中成功运输物体,包括走廊,多边形和迷宫.
  • 该方法允许无扩展到新的,大规模的环境,无需额外的培训或再学习.
  • 模拟验证了该方法在各种场景中的有效性和稳定性.
关键词:
深度强化学习的学习.物体运输物体运输物体运输物体任务空间的分解

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结论:

  • 这种新的DRL与TSD方法显著提高了机器人物体运输的可扩展性和适用性.
  • 这种方法解决了复杂和动态环境中的传统DRL方法的局限性.
  • 该方法为需要适应性的现实世界机器人操纵任务提供了强大的解决方案.