Dynamic Environment Mapping for Autonomous Thermal Soaring
Dynamic Environment Mapping for Autonomous Thermal Soaring
复制标题
自主热飙升的动态环境映射
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
S. Saripalli
中科院分区：
文献类型：
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作者：
C. Bower;Tristan C. Flanzer;A. Naiman;S. Saripalli
本文介绍了一种基于地图的，高层次的控制算法的自主热飙升。该算法结合了占用网格地图和机器人和机器学习社区中使用的值迭代算法的思想。整个八域的特殊能量率的估计是从三个子函数分量建立的。当没有已知的有利能量区域时，rst分量用于驱动勘探。短期记忆组件综合最近的传感器测量结果，以映射UAV附近可用的能量，并能够检测移动热气流并使其居中。nal组件使用先前大气能量测量的历史来识别ight域中的模式，并允许飞行器返回到始终形成热气流的位置。七个变化的高层次的控制算法进行了测试，使用六个自由度的模拟和相比，有界的情况下，控制器要么有完美的知识的热场或没有热传感能力和IES一个大直径的圆。感兴趣的目标是在一个小时的模拟中最大化飞机的平均能量状态。由于热模型的随机性质，对于控制算法的每个变化运行100次试验。仿真结果表明，平均而言，性能最好的自主热飙升控制器实现了62%的目标可能的改善。对于每种算法，由于热效应的随机性，所实现的性能存在很大的变化。
This paper describes a map-based, high-level control algorithm for autonomous thermal soaring. The algorithm combines ideas from occupancy grid maps and value iteration algorithms used in the robotics and machine learning communities. Estimates of the specic energy rate throughout the ight domain are built from three sub-function components. The rst component is used to drive exploration when no favorable energy regions are known. A short-term memory component synthesizes recent sensor measurements to map the energy available in the neighborhood of the UAV and enables the detection of and centering in moving thermals. The nal component uses a history of previous atmospheric energy measurements to identify patterns in the ight domain and allow the vehicle to return to locations that consistently form thermals. Seven variations on the high-level control algorithm are tested using a six degree of freedom simulation and compared to bounding cases where the controller either has perfect knowledge of the thermal eld or has no thermal sensing ability and ies a large diameter circle. The objective of interest is to maximize the aircraft’s average energy state over a one hour simulation. Due to the stochastic nature of the thermal model, 100 trials are run for each variation of control algorithm. Simulations indicate that on average the best performing autonomous thermal soaring controller achieves 62% of the possible improvement in the objective. For each algorithm there are large variations in the performance achieved due to the stochastic nature of the thermal elds.