Coupled Sensor Configuration and Path-Planning in Unknown Static Environments
Coupled Sensor Configuration and Path-Planning in Unknown Static Environments
复制标题
未知静态环境中的耦合传感器配置和路径规划
DOI：
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发表时间：
2021
期刊：
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通讯作者：
Raghvendra V. Cowlagi
中科院分区：
文献类型：
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作者：
Chase St. Laurent;Raghvendra V. Cowlagi
我们考虑通过传感器网络映射未知环境中的移动智能体的路径规划，其中每个传感器的位置和视野可以配置。为了解决这一问题，我们提出了一种传感器配置与路径规划（CSCP）耦合迭代方法，该方法在每次迭代中找到最优传感器配置（位置和视场），利用高斯过程回归构造威胁场估计，然后找到期望威胁暴露最小的候选最优路径。我们定义了一个所谓的任务驱动信息增益（TDIG）度量，其最大化提供了传感器配置。TDIG量化了获取与路径规划“最相关”的传感器数据的概念。当路径成本差异减少到低于预先指定的阈值时，CSCP迭代终止。通过数值模拟，我们证明了与传统方法相比，CSCP算法可以用更少的传感器测量值找到接近最优的路径。
We consider path-planning for a mobile agent in an unknown environment to be mapped by a sensor network, where the location and field of view of each sensor can be configured. To solve this problem we propose a coupled sensor configuration and path-planning (CSCP) iterative method, which finds an optimal sensor configuration (location and FoV) at each iteration, applies Gaussian process regression to construct a threat field estimate, and then finds a candidate optimal path with minimum expected threat exposure. We define a so-called task-driven information gain (TDIG) metric, the maximization of which provides sensor configurations. The TDIG quantifies the notion of acquiring sensor data of “most relevance” to path-planning. The CSCP iterations terminate when the path cost variance reduces below a prespecified threshold. Through numerical simulations we demonstrate that the CSCP algorithm finds near-optimal paths with significantly fewer sensor measurements compared to traditional methods.