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1. Efficient multi-robot coverage of a known environment

   https://doi.org/10.1109/IROS.2017.8206000  

   Karapetyan, Nare; Benson, Kelly; McKinney, Chris; Taslakian, Perouz; Rekleitis, Ioannis (September 2017, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   This paper addresses the complete area coverage problem of a known environment by multiple-robots. Complete area coverage is the problem of moving an end-effector over all available space while avoiding existing obstacles. In such tasks, using multiple robots can increase the efficiency of the area coverage in terms of minimizing the operational time and increase the robustness in the face of robot attrition. Unfortunately, the problem of finding an optimal solution for such an area coverage problem with multiple robots is known to be NP-complete. In this paper we present two approximation heuristics for solving the multi-robot coverage problem. The first solution presented is a direct extension of an efficient single robot area coverage algorithm, based on an exact cellular decomposition. The second algorithm is a greedy approach that divides the area into equal regions and applies an efficient single-robot coverage algorithm to each region. We present experimental results for two algorithms. Results indicate that our approaches provide good coverage distribution between robots and minimize the workload per robot, meanwhile ensuring complete coverage of the area. 

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2. On the depth-averaged models of ice-covered flows

   https://doi.org/10.1007/s10652-024-10003-3  

   Koyuncu, Berkay; Akerkouch, Lahcen; Le, Trung (August 2024, Environmental Fluid Mechanics)

   Abstract Impact of ice coverage is significant in controlling the depth-averaged velocity profile and influencing morphological processes in alluvial channels. However, this impact is largely unknown under field conditions. In this work, a numerical method is introduced to compute the depth-averaged velocity profile in irregular cross-sections of ice-covered flows, based on the Shiono-Knight approach. The momentum equation is modified to account for the presence of secondary flows and the ice coverage. The equations are discretized and solved with velocity boundary conditions at the bank and at one vertical. Our approach only requires the cross-section geometry and a single velocity measurement near the high-velocity region, offering a significant advantage in inaccessible locations by avoiding the need to measure the velocity profile in the entire cross-section. The proposed model is then validated using depth-averaged velocity profile and secondary flow patterns from laboratory observations, analytical solution, and Large-Eddy Simulation. Finally, the method is applied to infer depth-averaged velocity profiles in the Red River of the North, United States, to test its performance in meandering sections. The proposed method demonstrates its robustness in reconstructing flow profiles in ice-covered conditions with a minimal amount of available data, which is crucial for assessing erosion risks and managing spring floods in cold regions. 

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3. LQG Reference Tracking with Safety and Reachability Guarantees under False Data Injection Attacks

   Niu, L; Li, Z; Clark, A. (January 2019, American Control Conference (ACC))

   Control systems are increasingly targeted by malicious adversaries, who may inject spurious sensor measurements in order to bias the controller behavior and cause suboptimal performance or safety violations. This paper investigates the problem of tracking a reference trajectory while satisfying safety and reachability constraints in the presence of such false data injection attacks. We consider a linear, time-invariant system with additive Gaussian noise in which a subset of sensors can be compromised by an attacker, while the remaining sensors are regarded as secure. We propose a control policy in which two estimates of the system state are maintained, one based on all sensors and one based on only the secure sensors. The optimal control action based on the secure sensors alone is then computed at each time step, and the chosen control action is constrained to lie within a given distance of this value. We show that this policy can be implemented by solving a quadraticallyconstrained quadratic program at each time step. We develop a barrier function approach to choosing the parameters of our scheme in order to provide provable guarantees on safety and reachability, and derive bounds on the probability that our control policies deviate from the optimal policy when no attacker is present. Our framework is validated through numerical study. 

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4. Optimizing resource constrained distributed collaborative sensing

   G. de Veciana M. Stecklein, H. Beytur (June 2021, IEEE ICC Workshop on Spectrum Sharing Technology for Next Generation Communications)

   null (Ed.)

   Collaborative sensing of spatio-temporal events/processes is at the basis of many applications including e.g., spectrum and environmental monitoring, and self-driving cars. A system leveraging spatially distributed possibly airborn sensing nodes can in principle deliver better coverage as well as possibly redundant views of the observed processes. This paper focuses on modeling, characterising and quantifying the benefits of optimal sensor activation/scanning policies in resource constrained settings, e.g., constraints tied to energy expenditures or the scanning capabilities of nodes. Under a natural model for the process being observed we show that a periodic sensor activation policy is optimal, and characterize the relative phases of such policies via an optimization problem capturing knowledge of the sensor geometry, sensor coverage sets, and spatio-temporal intensity and event durations. Numerical and simulation results for simple different sensor geometries exhibit how performance depends on the underlying processes. We also study the gap between optimal and randomized policies and how it scales with the density of sensors and resource constraints. 

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5. Reinforcement learning-based optimal control of uncertain nonlinear systems

   https://doi.org/10.1080/00207179.2024.2305727  

   Garcia, Miguel; Dong, Wenjie (January 2024, International Journal of Control)

   This paper considers the optimal control of a second-order nonlinear system with unknown dynamics. A new reinforcement learning based approach is proposed with the aid of direct adaptive control. By the new approach actor-critic reinforcement learning algorithms are proposed with three neural network approximation. Simulation results are presented to show the effectiveness of the proposed algorithms. 

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