A coupled path-planning and sensor configuration method is proposed. The path-planning objective is to minimize exposure to an unknown spatially-varying scalar field, called the threat field, measured by a network of sensors. Gaussian Process regression is used to estimate the threat field from these measurements. Crucially, the sensors are configurable, i.e., parameters such as location and size of field of view can be changed. A main innovation of this work is that sensor configuration is performed by maximizing a so-called task-driven information gain (TDIG) metric, which quantifies uncertainty reduction in the cost of the planned path. For computational efficiency, a surrogate metric called the self-adaptive mutual information (SAMI) is introduced and shown to be submodular. The proposed method is shown to vastly outperform traditionally decoupled information-driven sensor configuration in terms of the number of measurements required to find near-optimal plans.
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Coupled Sensor Configuration and Path-Planning in a Multimodal Threat Field
A coupled path-planning and sensor configuration method is proposed. The path-planning objective is to minimize exposure to an unknown, spatially-varying, and temporally static scalar field called the threat field. The threat field is modeled as a weighted sum of several scalar fields, each representing a mode of threat. A heterogeneous sensor network takes noisy measurements of the threat field. Each sensor in the network observes one or more threat modes within a circular field of view (FoV). The sensors are configurable, i.e., parameters such as location and size of field of view can be changed. The measurement noise is assumed to be normally distributed with zero mean and a variance that monotonically increases with the size of the FoV, emulating the FoV v/s resolution trade-off in most sensors. Gaussian Process regression is used to estimate the threat field from these measurements. The main innovation of this work is that sensor configuration is performed by maximizing a so-called task-driven information gain (TDIG) metric, which quantifies uncertainty reduction in the cost of the planned path. Because the TDIG does not have any convenient structural properties, a surrogate function called the self-adaptive mutual information (SAMI) is considered. Sensor configuration based on the TDIG or SAMI introduces coupling with path-planning in accordance with the dynamic data-driven application systems paradigm. The benefit of this approach is that near-optimal plans are found with a relatively small number of measurements. In comparison to decoupled path-planning and sensor configuration based on traditional information-driven metrics, the proposed CSCP method results in near-optimal plans with fewer measurements.
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- Award ID(s):
- 2126818
- NSF-PAR ID:
- 10393834
- Editor(s):
- Blasch, Erik; Ravela, Sai
- Date Published:
- Journal Name:
- Dynamic Data Driven Application Systems Conference DDDAS2022
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
