• 해양환경안전학회:학술대회논문집
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• 해양환경안전학회 2017년도 추계학술발표회
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• pp.160-160
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• 2017

• International Journal of Aeronautical and Space Sciences
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• 제11권4호
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• 한국군사과학기술학회지
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• 제22권5호
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• pp.667-673
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• 2019

We present an orienteering problem with unknown stochastic reward(OPUSR) model for persistent monitoring tasks with unknown event probabilities at each point of interest. Prior studies on orienteering problem for persistent monitoring task assume that rewards and event probabilities are known as a prior. In this paper, we propose a stochastic reward model with unknown event statistics and a path re-planning algorithm based on Bayesian reward inference. Experiments demonstrate the efficiency of our method.