使用高斯过程预测安全机器人探索的不确定性规划
Alex Stephens1, Matthew Budd1, Michal Staniaszek1
1Oxford Robotics Institute, University of Oxford, Oxford, UK.
概括
这项研究引入了一个新的框架,用于在未知的环境中安全地进行机器人探索. 它使用高斯过程和马尔科夫决策过程来确保机器人在绘制新区域时保持在安全的操作限制内.
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
- 环境科学 环境科学
背景情况:
- 移动机器人探索对于绘制未知区域的地图至关重要.
- 确保机器人安全在预定义的环境条件值 (例如地形度,辐射) 内是一个重大挑战.
- 现有的方法往往难以同时绘制地图和安全探索.
研究的目的:
- 为移动机器人开发一种新的框架,用于在未知的环境中安全地探索.
- 解决两个场景:已知的地图与未知的安全特征,和未知的地图与未知的安全特征.
- 为了使机器人能够在遵守安全限制的同时构建地图.
主要方法:
- 利用高斯过程来预测未访问地区的环境特征值.
- 开发了一个马尔科夫决策过程,整合了高斯过程预测和环境模型过渡概率.
- 将马尔科夫决策过程纳入一个探索算法,优先考虑信息获取,预测安全和近距离.
主要成果:
- 拟议的框架有效地引导机器人探索新地区,同时保持安全.
- 通过模拟进行的实证评估证明了该框架的有效性.
- 在地下环境中的物理机器人上成功应用验证了这一方法.
结论:
- 开发的框架为复杂,未知的环境中安全勘探提供了强大的解决方案.
- 这种方法提高了机器人的自主性和在具有挑战性的地形上运营的安全性.
- 预测建模和决策过程的整合为未来的机器人探索系统提供了一个有希望的方向.
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