Robots such as autonomous underwater vehicles (AUVs) and autonomous surface vehicles (ASVs) have been used for sensing and monitoring aquatic environments such as oceans and lakes. Environmental sampling is a challenging task because the environmental attributes to be observed can vary both spatially and temporally, and the target environment is usually a large and continuous domain whereas the sampling data is typically sparse and limited. The challenges require that the sampling method must be informative and efficient enough to catch up with the environmental dynamics. In this paper we present a planning and learning method that enables a sampling robot to perform persistent monitoring tasks by learning and refining a spatiotemporal environmental model. Our environmental sampling framework consists of two components: to maximize the information collected, we propose an informative planning component that efficiently generates sampling waypoints that contain the maximal information; To alleviate the computational bottleneck caused by large-scale data accumulated, we develop a component based on a sparse Gaussian Process whose hyperparameters are learned online by taking advantage of only a subset of data that provides the greatest contribution. We validate our method with both simulations running on real ocean data and field trials with an ASV in a lake environment. Our experiments show that the proposed framework is both accurate and efficient in learning the spatiotemporal environmental model
Methodologies for environmental sensing and monitoring have also been developed to address challenging problems in different scenarios. For instance, stochastic search has been considered for switching fields @cite_17 , and Markov Chain Monte Carlo (MCMC) techniques have been used to study convergence @cite_19 . Convergence guarantees were also analyzed for control using gradient-aware straight line motions for point-mass vehicles @cite_60 . Distributed control methods based on gradient climbing @cite_28 , consensus @cite_10 , and source seeking under limited communication @cite_53 , have been developed using different optimization techniques. Further work using multiple vehicles @cite_18 took account of simplified turbulent flows @cite_29 and bio-inspired methods with stability guarantees @cite_13 .
@cite_13 @cite_18 @cite_60 @cite_28 @cite_53 @cite_29 @cite_19 @cite_10 @cite_17
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We develop a robust plume tracking strategy using mobile sensor networks in three dimensional (3D) fields. Inspired by the plume tracking behavior of blue crabs, we propose a stochastic model of plume spikes detected by sensing agents based on the Poisson counting process, which enables us to transform the turbulent plume field detected by sensing agents to a continuously-differential field, the minimum of which is considered as a source in the field, and shares the same location with the plume source. The transformation allows us to design and analyze source-seeking algorithms in smooth fields instead of in turbulent fields with higher fluctuation spatially and temporally. Inspired by fish schools that seek darker (shaded) regions in environments with complex lighting variations, we develop a distributed source-seeking algorithm using mobile sensor networks without explicit gradient estimation. The velocity of each agent is designed using only the measurements taken by the agent and the relative positions to its neighboring agents. We prove that, using this design, the moving direction of a three-agent group will converge to the opposite gradient direction of the field, thus, the group moves towards a source in the field. We also prove that the tracking system is input-to-state stable (ISS), indicating that the system is robust to disturbances. We then generalize the design to N-agent groups, and demonstrate the strategy in both smooth fields and turbulent fields in simulations. We developed a novel, multiple vehicle extension to a typical, single vehicle chemical plume source localization algorithm. The approach is implemented and tested in MATLAB®. We discuss simulations results to assess the potential of using the multiple vehicle approach. It occurs that there can be an advantage from using multiple vehicles. We consider the problem of steering an autonomous vehicle to locate a radiation source utilizing measurements of the radiation intensity only. We propose a control algorithm that locates the source through a sequence of line minimizations of the radiation intensity. We implement in a hybrid controller, with sample-and-hold and logic variables, a discretized version of the algorithm suitable for steering a point-mass vehicle. The algorithm confers global convergence and practical stability properties to the closed-loop hybrid system. We discuss these properties and characterize the region of convergence for the vehicle. Convergence and stability results are supplemented with simulations. We present a stable control strategy for groups of vehicles to move and reconfigure cooperatively in response to a sensed, distributed environment. Each vehicle in the group serves as a mobile sensor and the vehicle network as a mobile and reconfigurable sensor array. Our control strategy decouples, in part, the cooperative management of the network formation from the network maneuvers. The underlying coordination framework uses virtual bodies and artificial potentials. We focus on gradient climbing missions in which the mobile sensor network seeks out local maxima or minima in the environmental field. The network can adapt its configuration in response to the sensed environment in order to optimize its gradient climb. We develop a plume tracking algorithm for a swarm of mobile sensing agents in turbulent flow. Inspired by blue crabs, we propose a stochastic model for plume spikes based on the Poisson counting process, which captures the turbulent characteristic of plumes. We then propose an approach to estimate the parameters of the spike model, and transform the turbulent plume field detected by sensing agents into a smoother scalar field that shares the same source with the plume field. This transformation allows us to design path planning algorithms for mobile sensing agents in the smoother field instead of in the turbulent plume field. Inspired by the source seeking behaviors of fish schools, we design a velocity controller for each mobile agent by decomposing the velocities into two perpendicular parts: the forward velocity incorporates feedback from the estimated spike parameters, and the side velocity keeps the swarm together. The combined velocity is then used to plan the path for each agent in the swarm. Theoretical justifications are provided for convergence of the agent group to the plume source. The algorithms are also demonstrated through simulations. We propose a new method for locating the source(s) of a fluid that is diffusing into sea water. Our method utilizes multiple Autonomous Underwater Vehicles (AUVs) whose motion is controlled via a discrete-time Markov Chain Monte Carlo (MCMC) algorithm. The MCMC algorithm relies only on local measurements of the concentration of the fluid to construct and estimate of the concentration field over the search domain, and hence localize the source(s). We prove the existence of an invariant measure for the Markov chain that is generated by the closed-loop motion of the vehicles. The convergence rate of the Markov chain is investigated through extensive numerical simulations. We consider the problem of designing controllers to steer mobile robots to the source (the minimizer) of a signal field. In addition to the mobility constraints, e.g., posed by the nonholonomic dynamics, we assume that the field is completely unknown to the robot and the robot has no knowledge of its own position. Furthermore, the unknown field is randomly switching. In the case where the information of the field (e.g., the gradient) is completely known, standard motion planning techniques for mobile robots would converge to the known source. In the absence of mobility constraints, convergence to the minimum of unknown fields can be pursued using the framework of numerical optimization. By considering these facts, this paper exploits an idea of the stochastic approximation for solving the problem mentioned in the beginning and proposes a source seeking controller which sequentially generates the next waypoints such that the resulting discrete trajectory converges to the unknown source and which steers the robot along the waypoints, under the assumption that the robot can move to any point in the body fixed coordinate frame. To this end, we develop a rotation-invariant and forward-sided version of the simultaneous-perturbation stochastic approximation algorithm as a method to generate the next waypoints. Based on this algorithm, we design source seeking controllers. Furthermore, it is proven that the robot converges to a small set including the source in a probabilistic sense if the signal field switches periodically and sufficiently fast. The proposed controllers are demonstrated by numerical simulations.