用辅助演员歧视器进行自动驾驶的深度强化学习
Qiming Gao1, Fangle Chang1,2, Jiahong Yang1,3
1Ningbo Innovation Center, Zhejiang University, Ningbo 315100, China.
Sensors (Basel, Switzerland)
|January 26, 2024
概括
本研究介绍了一种使用状态注意网络 (SAN) 和辅助行为者区分器 (AAD) 的新型机器人导航系统,用于在动态环境中高效地规划路径和避开障碍物.
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:
- 对智能机器人来说,路径规划和避开障碍物至关重要,特别是在未知的动态环境中.
- 现有的方法往往难以在这样复杂的场景中所需的灵活性和快速决策.
研究的目的:
- 开发一种先进的机器人导航系统,能够有效地规划路径和避开障碍物.
- 增强机器人的决策和在未知的动态环境中的探索能力.
主要方法:
- 一个状态注意网络 (SAN) 已开发用于机器人-障碍物交互的特征提取.
- 实施了辅助演员区分器 (AAD) 来计算碰撞概率.
- 使用以启发式知识 (HK) 为指导的以目标为导向和基于差距的导航策略.
- 软行动者-批判 (SAC) 算法用于模拟环境中的政策培训.
主要成果:
- 拟议的方法表明了对机器人系统最佳行动策略的趋同.
- 机器人探索未知的环境的移动步骤显著减少,显示下降33.9%.
- 该系统实现了更高的平均回报,与其他方法相比增加了29.15%.
结论:
- 开发的导航系统在复杂,未知的动态环境中提高了机器人的性能.
- SAN,AAD和启发式知识的整合为智能机器人探索提供了一个强大的解决方案.
- 这项研究有助于更高效和有效的自主机器人导航.
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