Unmanned Aerial Vehicles (UAVs) have been implemented for environmental monitoring by using their capabilities of mobile sensing, autonomous navigation, and remote operation. However, in real-world applications, the limitations of on-board resources (e.g., power supply) of UAVs will constrain the coverage of the monitored area and the number of the acquired samples, which will hinder the performance of field estimation and mapping. Therefore, the issue of constrained resources calls for an efficient sampling planner to schedule UAV-based sensing tasks in environmental monitoring. This paper presents a mission planner of coverage sampling and path planning for a UAV-enabled mobile sensor to effectively explore and map an unknown environment that is modeled as a random field. The proposed planner can generate a coverage path with an optimal coverage density for exploratory sampling, and the associated energy cost is subjected to a power supply constraint. The performance of the developed framework is evaluated and compared with the existing state-of-the-art algorithms, using a real-world dataset that is collected from an environmental monitoring program as well as physical field experiments. The experimental results illustrate the reliability and accuracy of the presented coverage sampling planner in a prior survey for environmental exploration and field mapping.
@cite_0: The remarkable accessibility of modern flying robots makes them an attractive platform for environmental sensing. However, low cost and ease of use are currently incompatible with large payloads, severely limiting the choice of sensor and ultimately modality. This paper describes the design of a system for using a small infrared thermometer to estimate the surface temperature over an area that is large compared to the area measured by the sensor, by mounting it on a flying robot. We leverage a priori knowledge about the spatial statistics of the phenomena under measure in order to plan an informative sampling path, fusing observations by Gaussian process regression. Our approach is designed to be evaluated in an indoor testbed, in which a quadrotor, in cooperation with simulated static sensing nodes, estimates the spatial distribution of surface temperature over a controlled thermal gradient. We perform extensive systematic experimentation both in simulation and our real-world testbed environment, with our algorithm estimating surface temperature to an accuracy of up to 2.1 °C over a 16 m2 area ranging in value from 25–65 °C.
@cite_21: This paper addresses the problem of driving robotic sensors for an energy-constrained mobile wireless network in efficiently monitoring and predicting spatial phenomena, under data locational errors. The paper first discusses how errors of mobile sensor locations affect estimating and predicting the spatial physical processes, given that spatial field to be monitored is modeled by a Gaussian process. It then proposes an optimality criterion for designing optimal sampling paths for the mobile robotic sensors given the localization uncertainties. Although the optimization problem is optimally intractable, it can be resolved by a polynomial approximation algorithm, which is proved to be practically feasible in an energy-constrained mobile sensor network. More importantly, near-optimal solutions of this navigation problem are guaranteed by a lower bound within @math of the optimum. The performance of the proposed approach is evaluated on simulated and real-world data sets, where impact of sensor location errors on the results is demonstrated by comparing the results with those obtained by using noise-less data locations.
A distribution of sampling locations provides a pattern across the study area, which indicates the estimation performance of the underlying field. Many frameworks have been proposed to select the target locations for constructing the scalar field. Among them, the experimental design theory has been studied to find the optimum sensor deployment plan by optimizing an information gain that is determined by using an established model. In the research, strategies were addressed to select the locations with high informativeness'' to achieve adaptive sensing (e.g., @cite_21 ). However, these strategies rely on prior knowledge of the environmental model structure. Deviations of the assumptions will lead to model misspecification and unacceptable estimation performance. In other words, the field estimation results will not be robust against misspecification of the established model. In many circumstances, the assumptions may not be practically feasible. For instance, the assumption of a known and constant mean of the environmental model (e.g., @cite_0 ) can cause inferior estimation performance in a practical situation where unknown spatial trends exist.