移动机器人的大脑启发的行为决策基于动机发育网络
Xudong Lv1, Qi Liu1, Dongshu Wang1
1School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China.
概括
这项研究引入了一个增强的深度神经网络,用于移动机器人在未知的环境中做出决策. 该方法提高了学习效率和长期适应能力,用于复杂的路径规划.
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
- 计算神经科学是一种神经科学.
背景情况:
- 在未知的环境中移动机器人导航在计算效率和长期学习方面提出了挑战.
- 现有的方法与高维空间作斗争,并随着时间的推移保持适应性.
研究的目的:
- 为移动机器人提出一个改进的行为决策模型.
- 提高计算效率,长期学习能力和在未知的环境中适应能力.
主要方法:
- 集成一个深度神经网络与神经重塑机制和一个自适应的步骤大小策略.
- 激励发育网络和深度Q网络目标网络的加权集成,以改进Q值估计.
- 神经重塑机制以重置神经元年龄并减轻学习衰退.
- 根据障碍物的近距离进行优化路径规划的适应性步骤大小策略.
主要成果:
- 提高Q值估计的可靠性,提高培训稳定性和融合速度.
- 缓解学习能力下降,保持机器人的适应性和学习能力.
- 通过动态加速和减速优化路径规划性能.
- 在整体行为决策模型中提高灵活性和稳定性.
结论:
- 拟议的混合模型显著改善了移动机器人在未知的环境中的决策.
- 深度学习,神经重塑和自适应策略的整合为导航挑战提供了强大的解决方案.
- 模拟和物理实验证实了模型在现实世界中应用的潜力.
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