关于自主搜索目标的可能论式
Zhijin Chen1, Branko Ristic1, Du Yong Kim1
1School of Engineering, RMIT University, 376-392 Swanston Street, Melbourne, VIC 3000, Australia.
Entropy (Basel, Switzerland)
|June 26, 2024
概括
这项研究引入了一种新的自主搜索方法,使用可能性理论,在不确定性下增强目标定位. 新方法为部分已知的检测概率提供了改进的定量建模.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 信息理论和信号处理.
背景情况:
- 自主搜索依赖于传感,估计和运动控制来定位目标.
- 传统方法使用贝叶斯估计和信息理论,这可能与认识体系的不确定性作斗争.
研究的目的:
- 在可能性理论的框架内制定自主搜索.
- 解决在搜索操作中的认识体系不确定性所带来的定量建模和推理挑战.
主要方法:
- 开发了一种自主搜索的可能性学公式.
- 引入了一种类似贝叶斯的解决方案,用于顺序估计.
- 定义了一个运动控制奖励函数,考虑到认识体系的不确定性.
主要成果:
- 对部分已知的检测概率 (区间值) 进行了量化建模.
- 展示了一种优雅的贝叶斯式估计方法.
- 通过数值模拟验证了增强的搜索算法的优势.
结论:
- 可能性理论为具有认识体系不确定性的自主搜索提供了一个强大的框架.
- 拟议的算法为目标本地化提供了更复杂的方法.
- 该方法在没有完整检测信息的场景中是有效的.
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