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Mobile sensor networks can be employed in multiple applications, such as search and rescue, border patrol, battle scenarios, and environmental monitoring. In this survey, we review the literature utilizing mobile sensor networks in applications classified as target searching and/or tracking. Our contribution is threefold. First, we focus on the diverse types of filters applied to estimating the state of the targets. Second, we present the most common approaches to high-level trajectory planning for the sensors in the network to do target searching and/or tracking. Finally, we classify the literature based on the problem formulation used and solution characteristics. At the end of the survey, we discuss the current state of the literature and possible directions for future research efforts.

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As oil exploration and development costs rise, the oil industry increases its efforts to improve oil recovery (IOR) from existing fields. IOR is achieved mainly by drilling more wells, but drilling in partially depleted reservoirs is challenging due to narrow pressure margins. Offshore drilling in harsh environments, such as the North Sea, presents additional challenges, since the heaving motion from a floating rig induces large surge and swab pressures in the well. The paper suggests a remedy for this problem using automatic control of well pressure. Taking advantage of an experimental lab facility recently completed at NTNU, a model of the drilling system is developed using subspace identification methods. The model serves as a basis for state estimation and controller design using model predictive control. Applying the controller to the lab facility, pressure oscillations are suppressed by 70-90% compared to the open-loop case, depending on the period of the heave motion Index TermsManaged pressure drilling, constant bottomhole pressure, disturbance attenuation, model predictive control

Combined searching and tracking of objects using Unmanned Aerial Vehicles (UAVs) is an important task with many applications. One way to approach this task is to formulate path-planning as a continuous optimal control problem. However, such formulations will, in general, be complex and difficult to solve with global optimality. Therefore, we propose a two-layer framework, in which the first layer uses a Traveling-Salesmantype formulation implemented using combinatorial optimization to find a near-globally-optimal path. This path is refined in the second layer using a continuous optimal control formulation that takes UAV dynamics and constraints into consideration. Searching and tracking problems usually trade-off, often in a manual or ad-hoc manner, between searching unexplored areas and keeping track of already known objects. Instead, we derive a result that enables prioritization between searching and tracking based on the probability of finding a new object weighted against the probability of losing tracked objects. Based on this result, we construct a new algorithm for searching and tracking. This algorithm is validated in simulation, where it is compared to multiple base cases as well as a case utilizing perfect knowledge of the positions of the objects. The simulations demonstrate that the algorithm performs significantly better than the base cases, with an improvement of approximately 5-15%, while it is approximately 20-25% worse than the perfect case.

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Abstract-In this paper we calculate probabilistic estimates for the size of an area a single unmanned aerial vehicle (UAV) can expect to monitor when tracking multiple objects. The objects are assumed to move according to a linear velocity model with Gaussian process noise. We use a Kalman filter to estimate the position of the objects. By using the covariance matrix of the Kalman filter, we can derive the necessary visitation period for a UAV to have a probability within a given confidence interval of redetecting the object at the estimated position. Then, we use this visitation period to calculate the probabilistic estimate for the area a single UAV can monitor. We demonstrate the results in Monte Carlo simulations.

As oil exploration and development costs rise, the oil industry increases its efforts to improve oil recovery (IOR) from existing fields. IOR is achieved mainly by drilling more wells, but drilling in partially depleted reservoirs is challenging due to narrow pressure margins. Offshore drilling in harsh environments, such as the North Sea, presents additional challenges, since the heaving motion from a floating rig induces large surge and swab pressures in the well. The paper suggests a remedy for this problem using automatic control of well pressure. Taking advantage of an experimental lab facility recently completed at NTNU, a model of the drilling system is developed using subspace identification methods. The model serves as a basis for state estimation and controller design using model predictive control. Applying the controller to the lab facility, pressure oscillations are suppressed by 70-90% compared to the open-loop case, depending on the period of the heave motion.