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1. The Convex Uncertain Voronoi Diagram for Safe Multi-Robot Multi-Target Tracking Under Localization Uncertainty

   https://doi.org/10.1007/s10846-023-01986-0  

   Chen, Jun; Dames, Philip (December 2023, Journal of Intelligent & Robotic Systems)

   Accurately detecting, localizing, and tracking an unknown and time-varying number of dynamic targets using a team of mobile robots is a challenging problem that requires robots to reason about the uncertainties in their collected measurements. The problem is made more challenging when robots are uncertain about their own states, as this makes it difficult to both collectively localize targets and avoid collisions with one another. In this paper, we introduce the convex uncertain Voronoi (CUV) diagram, a generalization of the standard Voronoi diagram that accounts for the uncertain pose of each individual robot. We then use the CUV diagram to develop distributed multi-target tracking and coverage control algorithms that enable teams of mobile robots to account for bounded uncertainty in the location of each robot. Our algorithms are capable of safely driving mobile robots towards areas of high information distribution while maintaining coverage of the whole area of interest. We demonstrate the efficacy of these algorithms via a series of simulated and hardware tests, and compare the results to our previous work which assumes perfect localization. 

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2. Cooperative Filtering and Parameter Identification for Advection–Diffusion Processes Using a Mobile Sensor Network

   https://doi.org/10.1109/TCST.2022.3183585  

   You, Jie; Zhang, Ziqiao; Zhang, Fumin; Wu, Wencen (June 2022, IEEE Transactions on Control Systems Technology)

   This article presents an online parameter identification scheme for advection-diffusion processes using data collected by a mobile sensor network. The advection-diffusion equation is incorporated into the information dynamics associated with the trajectories of the mobile sensors. A constrained cooperative Kalman filter is developed to provide estimates of the field values and gradients along the trajectories of the mobile sensors so that the temporal variations in the field values can be estimated. This leads to a co-design scheme for state estimation and parameter identification for advection-diffusion processes that is different from comparable schemes using sensors installed at fixed spatial locations. Using state estimates from the constrained cooperative Kalman filter, a recursive least-square (RLS) algorithm is designed to estimate unknown model parameters of the advection-diffusion processes. Theoretical justifications are provided for the convergence of the proposed cooperative Kalman filter by deriving a set of sufficient conditions regarding the formation shape and the motion of the mobile sensor network. Simulation and experimental results show satisfactory performance and demonstrate the robustness of the algorithm under realistic uncertainties and disturbances. 

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3. Using mobile sensor density to approximate state feedback controllers for a class of PDEs

   https://doi.org/10.1016/j.ifacol.2022.10.388  

   Demetriou, Michael A.; Hu, Weiwei (January 2022, IFAC-PapersOnLine)

   . (Ed.)

   This work is concerned with the use of mobile sensors to approximate and replace the full state feedback controller by static output feedback controllers for a class of PDEs. Assuming the feedback operator associated with the full-state feedback controller admits a kernel representation, the proposed optimization aims to approximate the inner product of the kernel and the full state by a finite sum of weighted scalar outputs provided by the mobile sensors. When the full state feedback operator is time-dependent thus rendering its associated kernel time-varying, the approximation results in moving sensors with time-varying static gains. To calculate the velocity of the mobile sensors within the spatial domain the time-varying kernel is set equal to the sensor density and thus the solution to an associated advection PDE reveals the velocity field of the sensor network. To obtain the speed of the finite number of sensors, a domain decomposition based on a modification of the Centroidal Voronoi Tessellations (µ-CVT) is used to decompose the kernel into a finite number of cells, each of which contains a single sensor. A subsequent application of the µ-CVT on the velocity field provides the individual sensor speeds. The nature of this µ-CVT ensures collision avoidance by the very structure of the kernel decomposition into non-intersecting cells. Numerical simulations are provided to highlight the proposed sensor guidance. 

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4. Using Personal Exposure Measurement to Manage Environmental Stressors

   https://doi.org/10.63044/w25and110  

   Andrews, Clinton; Shahid, Yousaf; Andrews, Abigail; Gao, Ge; Gong, Jie; Josephs, Holly; Kim, Sunyoung; Li, Yitong; Maddila, Vijay; Mainelis, Gediminas; et al (May 2025, ASHRAE)

   Personal exposures to environmental stressors including extreme heat and air pollution vary widely depending on schedules and activities. This paper shares results of a city-scale project to build fixed indoor and outdoor sensor networks while also deploying mobile sensors. The network helps building occupants, building operators, and public officials to safely manage extreme heat and air pollution. The Exposure Duration Curve (EDC) concept is introduced to facilitate comparisons. 

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5. Optimal Coupled Sensor Placement and Path Planning in Unknown Time-Varying Environments

   https://doi.org/10.2514/1.G009081  

   Poudel, Prakash; Cowlagi, Raghvendra V (January 2025, Journal of Guidance, Control, and Dynamics)

   We address path planning for a mobile agent to navigate in an unknown environment with minimum exposure to a spatially and temporally varying threat field. The threat field is estimated using pointwise noisy measurements from a mobile sensor network. For this problem, we present a new information gain measure for optimal sensor placement that quantifies reduction in uncertainty in the path cost rather than the environment state. This measure, which we call the context-relevant mutual information (CRMI), couples the sensor placement and path-planning problem. We propose an iterative coupled sensor configuration and path-planning (CSCP) algorithm. At each iteration, this algorithm places sensors to maximize CRMI, updates the threat estimate using new measurements, and recalculates the path with minimum expected exposure to the threat. The iterations converge when the path cost variance, which is an indicator of risk, reduces below a desired threshold. We show that CRMI is submodular, and therefore greedy optimization provides near-optimal sensor placements while maintaining computational efficiency of the CSCP algorithm. Distance-based sensor reconfiguration costs are introduced in a modified CRMI measure, which we also show to be submodular. Through numerical simulations, we demonstrate that the principal advantage of this algorithm is that near-optimal low-variance paths are achieved using far fewer sensor measurements as compared to a standard sensor placement method. 

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