在使用移动机器人对未知的空间场进行适应性信息采样时进行勘探-开发权衡
Aiman Munir1, Ramviyas Parasuraman1
1School of Computing, University of Georgia, Athens, GA 30602, USA.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
移动机器人使用自适应性信息采样来进行高效的映射. 与能源限制平衡勘探和开发是准确感测和定位的关键.
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
- 传感器网络 传感器网络
背景情况:
- 适应性信息采样可实现高效的移动机器人路点选择,用于准确的物理过程映射.
- 现有的方法往往忽略了机器人的能量限制,或者假定统一的能量容量.
研究的目的:
- 分析信息函数参数对适应性采样中勘探开发权衡的影响.
- 研究移动机器人的绘图精度,定位和能源效率之间的平衡.
主要方法:
- 利用高斯过程回归 (GPR) 来预测空间场,并估计信息性可信度边界.
- 评估信息功能参数对地图准确度 (RMSE),信心 (变异),能源消耗和单机和多机器人场景中的时间的影响.
主要成果:
- 证明,只优先考虑不确定性或预测平均值,对能源效率或绘图准确性来说分别是不利的.
- 通过强调信息不确定性 (例如,75:25的比例) 来确定一个最佳的权衡,以实现平衡的勘探和开采.
- 展示了信息函数参数对预测准确度,信心界限,能源使用和样本数量的影响.
结论:
- 最佳的能量感知信息函数参数对于实现特定的传感目标至关重要,例如源定位或映射.
- 一个平衡的方法,优先考虑一些价值的不确定性,有效地管理能源需求.
- 结果为在能源受限的移动机器人探索和绘图任务中选择合适的参数提供了洞察力.
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