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1. Relay Pursuit for Multirobot Target Tracking on Tile Graphs

   https://doi.org/10.1109/ICRA48891.2023.10161532  

   Mandal, Shashwata; Bhattacharya, Sourabh (May 2023, International conference on robotics and automation)

   In this work, we address a visbility-based target tracking problem in a polygonal environment in which a group of mobile observers try to maintain a line-of-sight with a mobile intruder. We build a bridge between data mining and visibility-based tracking using a novel tiling scheme for the polygon. First, we propose a tracking strategy for a team of guards located on the tiles to dynamically track an intruder when complete coverage of the polygon cannot be ensured. Next, we propose a novel variant of the Voronoi Diagram to construct navigation strategies for a team of co-located guards to track an intruder from any initial position in the environment. We present empirical analysis to illustrate the efficacy of the proposed tiling scheme. Simulations and testbed demonstrations are present in a video attachment. 

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2. Scalable Multi-Agent Surveillance: a Kernel-Based Approach

   https://doi.org/10.1109/ICRA55743.2025.11127616  

   Mandal, Shashwata; Bhattacharya, Sourabh (May 2025, IEEE)

   In this work, we address the deployment problem for a team of mobile guards that tries to maintain a line-ofsight with an unpredictable mobile intruder. First, we present a computationally efficient strategy for generating a set of points, called kernel points, that covers the entire polygon. We then introduce a polygon partitioning technique based on the location of the kernel points. Next, we propose control laws for a free guard to track an intruder in general polygonal environments based on the analysis of a pursuit-evasion game around a single corner [1]. Finally, we present several variations of the proposed control laws that include capture and search, and illustrate the improvement in the overall visual footprint of the team of mobile guards based on extensive simulations 

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3. Path planning with Incremental Roadmap Update for Visibility-based Target Tracking

   https://doi.org/10.1109/IROS40897.2019.8967602  

   Laguna, Guillermo J.; Bhattacharya, Sourabh (November 2019, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   In this paper, we address the visibility-based target tracking problem in which a mobile observer moving along a p-route, which we define as a fixed path for target tracking, tries to keep a mobile target in its field-of-view. By drawing a connection to the watchman's route problem, we find a set of conditions that must be satisfied by the p-route. Then we propose a metric for tracking to estimate a sufficient speed for the observer given the geometry of the environment. We show that the problem of finding the p-route on which the observer requires minimum speed is computationally intractable. We present a technique to find a p-route on which the observer needs at most twice the minimum speed to track the intruder and a reactive motion strategy for the observer. 

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4. ASTRO: A System for Off-grid Networked Drone Sensing Missions

   https://doi.org/10.1145/3464942  

   Petrolo, Riccardo; Shaikhanov, Zhambyl; Lin, Yingyan; Knightly, Edward (November 2021, ACM Transactions on Internet of Things)

   We present the design, implementation, and experimental evaluation of ASTRO, a modular end-to-end system for distributed sensing missions with autonomous networked drones. We introduce the fundamental system architecture features that enable agnostic sensing missions on top of the ASTRO drones. We demonstrate the key principles of ASTRO by using on-board software-defined radios to find and track a mobile radio target. We show how simple distributed on-board machine learning methods can be used to find and track a mobile target, even if all drones lose contact with a ground control. Also, we show that ASTRO is able to find the target even if it is hiding under a three-ton concrete slab, representing a highly irregular propagation environment. Our findings reveal that, despite no prior training and noisy sensory measurements, ASTRO drones are able to learn the propagation environment in the scale of seconds and localize a target with a mean accuracy of 8 m. Moreover, ASTRO drones are able to track the target with relatively constant error over time, even as it moves at a speed close to the maximum drone speed. 

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5. Active target tracking with self-triggered communications

   https://doi.org/10.1109/ICRA.2017.7989244  

   Zhou, Lifeng; Tokekar, Pratap (May 2017, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA))

   We study the problem of reducing the amount of communication in a distributed target tracking problem. We focus on the scenario where a team of robots are allowed to move on the boundary of the environment. Their goal is to seek a formation so as to best track a target moving in the interior of the environment. The robots are capable of measuring distances to the target. Decentralized control strategies have been proposed in the past that guarantee that the robots asymptotically converge to the optimal formation. However, existing methods require that the robots exchange information with their neighbors at all time steps. Instead, we focus on reducing the amount of communication among robots. We propose a self-triggered communication strategy that decides when a particular robot should seek up-to-date information from its neighbors and when it is safe to operate with possibly outdated information from the neighbor. We prove that this strategy converges to an optimal formation. We compare the two approaches (constant communication and self-triggered communication) through simulations of tracking stationary and mobile targets. 

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