相关实验视频

Updated: Jul 4, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

一个算法来创建模型文件的部分可观察的马尔科夫决策过程为移动机器人路径规划的算法

Shripad V Deshpande¹, R Harikrishnan¹, Jahariah Sampe²

¹Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.

|February 1, 2024

概括

本研究介绍了PCMRPP软件,用于管理部分可观测马尔科夫决策过程 (POMDP) 矩阵大小. 它为灵活的机器人路径规划解决方案提供了对离散和观察传播的控制.

科学领域:

机器人和人工智能机器人和人工智能
决策理论和优化方法

背景情况:

部分可观测的马尔科夫决策过程 (POMDP) 对于在不确定性下做出决策至关重要,但由于维度的诅咒,它们在计算上具有挑战性.
现有的方法往往专注于大POMDP的近似解决方案,忽视对矩阵大小的直接控制,这使手动模型创建复杂化.

研究的目的:

开发一种用于编程生成具有可控制尺寸和稀疏度的POMDP矩阵的方法.
为应对移动机器人路径规划等应用程序创建高维POMDP模型的挑战.

主要方法:

在PCMRPP软件包中实现了一种新的算法.
通过可配置状态组件的离散细分化来控制POMDP矩阵大小.
通过可配置的观察概率分布的扩散来控制矩阵稀疏性.

主要成果:

PCMRPP软件可以通过程序生成POMDP矩阵.
用户可以调整矩阵大小和稀疏度,在管理计算复杂性方面提供灵活性.
便于在POMDP解决方案的时间复杂性和其稳定性之间进行权衡.

结论:

PCMRPP为POMDP矩阵生成提供了灵活的方法,减轻了维度的诅咒.
该软件通过启用尺寸控制来增强POMDPs在移动机器人路径规划等领域的实际应用.
提供了一个新的解决方案,在不确定的环境中平衡计算效率和解决方案稳定性.

关键词:

移动机器人移动机器人避免障碍避免障碍避免障碍这就是PCMRPPPP.这就是POMDPDP.路径规划路径规划路径规划概率论技术是一种概率论技术.不确定性不确定性

更多相关视频

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

Published on: August 2, 2018

Operation of the Collaborative Composite Manufacturing CCM System

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Steps in the Modeling Process

Steps in the Modeling Process

Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...