Search for: All records

We address path planning for a mobile agent to navigate in an unknown environment with minimum exposure to a spatially and temporally varying threat field. The threat field is estimated using pointwise noisy measurements from a mobile sensor network. For this problem, we present a new information gain measure for optimal sensor placement that quantifies reduction in uncertainty in the path cost rather than the environment state. This measure, which we call the context-relevant mutual information (CRMI), couples the sensor placement and path-planning problem. We propose an iterative coupled sensor configuration and path-planning (CSCP) algorithm. At each iteration, this algorithm places sensors to maximize CRMI, updates the threat estimate using new measurements, and recalculates the path with minimum expected exposure to the threat. The iterations converge when the path cost variance, which is an indicator of risk, reduces below a desired threshold. We show that CRMI is submodular, and therefore greedy optimization provides near-optimal sensor placements while maintaining computational efficiency of the CSCP algorithm. Distance-based sensor reconfiguration costs are introduced in a modified CRMI measure, which we also show to be submodular. Through numerical simulations, we demonstrate that the principal advantage of this algorithm is that near-optimal low-variance paths are achieved using far fewer sensor measurements as compared to a standard sensor placement method. 

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. 

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. 

We present an adaptive fast-approximation for sensor configuration which finds near-optimal placements and sensor field of views (FoV). The fast-approximation, either via partition-based or density-based cluster analysis, adapts based on the relation between statistical uncertainty of the path plan and environmental uncertainty. The sensor configurations are performed over regions of interest which most directly influence the path-planning efforts. These regions of interest can include exploratory paths by sampling the probabilistic environment model. The path-planning efforts aim to decide upon a path which minimizes an agent’s exposure to threats in an unknown static environment. The noisy sensor network observations are used to construct a threat field estimate using Gaussian Process Regression each iteration with a stationary kernel and heteroscedastic gaussian likelihood. The optimization of a task-driven information gain determines optimal sensor configurations when maximized. The numerical performance of the direct optimization and the adaptive cluster analysis method is presented. Finally, we show that the cluster centers can be utilized as a dimensionality reduction technique for FoV optimization whereby we only optimize FoV radial coverage.