[1]王东署,杨凯.基于状态转移学习的机器人行为决策认知模型[J].郑州大学学报(工学版),2021,42(06):8-14.[doi:10.13705/j.issn.1671-6833.2021.04.012]
　Wang Dongjun,Yang Kai,Behavior Decision-making Cognitive Model of Mobile Robot Based on State Transfer Learning[J].Journal of Zhengzhou University (Engineering Science),2021,42(06):8-14.[doi:10.13705/j.issn.1671-6833.2021.04.012]

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基于状态转移学习的机器人行为决策认知模型()

分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:

42卷

期数:

2021年06期

页码:

8-14

栏目:

出版日期:

2021-11-10

文章信息/Info

Title:

Behavior Decision-making Cognitive Model of Mobile Robot Based on State Transfer Learning

作者:

王东署; 杨凯

郑州大学电气工程学院;

Author(s):

Wang Dongjun; Yang Kai;

School of Electrical Engineering, Zhengzhou University;

关键词:

行为决策; 状态转移; 发育神经网络; 连续学习; 适应性

Keywords:

behavior decision-making;  state transfer;  developmental neural network;  continuous learning;  adaptability

分类号:

TP242. 6

DOI:

10.13705/j.issn.1671-6833.2021.04.012

文献标志码:

A

摘要:

模拟人脑认知发育的过程，提出一种新的机器人行为决策认知模型。使用少量样本对发育网络进行训练，使其具备一些基本的行为决策能力。在每次执行任务结束后，机器人进入非工作状态，此时会触发状态转移学习机制。网络内部产生很多环境和决策的状态组合。通过评价机制可以找出能够针对某种环境采取合适决策的状态组合，网络经过对各种状态组合的学习，提高了在各种环境状态下的决策能力。通过发育网络中间层神经元的侧向激励机理，，记忆的知识量不断增多。该方法克服了传统机器人在未知环境中的适应性差以及针对不同的环境需要重新编程等问题。实验结果表明，这种发育模型能明显地改善机器人在未知环境下的适应性，使机器人可以通过不断积累经验应对各种复杂环境，提高行为决策的效果。

Abstract:

Due to the small sample size of traditional neural networks, the error rate of the recognition of the scene was very high, and it could not continuously learn during the execution of the task. This would lead to poor adaptability of traditional neural networks to unfamiliar environments. In response to these problems, a bionic robot behavior decision-making cognitive computing model was proposed. The algorithm used semi-supervised and state transition learning methods. Firstly, a small number of training samples were used to train the developmental neural network, so that it could have some basic behavioral decision-making capabilities. When the robot was exploring in the actual environment, it could continuously learn new unlearned scene data. When the robot completed the task, the network model would recall the specific scene it had experienced according to a certain probability, and combined the state transfer mechanism to continuously adjusted its own decision-making effect. This method could make the network model quickly converge to a stable state, and had strong adaptability in unknown environments. In order to verify the feasibility of the model, a real robot operating environment was designed, and the RIKIROBOT was used for navigation testing. Experimental results showed that this developmental model could converge to a stable state after 3 to 5 decision-making adjustments in an unknown environment, and the decision-making effect was continuously improved. Robots could deal with various complex environments by continuously accumulating knowledge, and had strong adaptability in unknown environments.

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