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1. Active Target Tracking With Self-Triggered Communications in Multi-Robot Teams

   https://doi.org/10.1109/TASE.2018.2867189  

   Zhou, Lifeng; Tokekar, Pratap (September 2018, IEEE Transactions on Automation Science and Engineering)

   We study the problem of reducing the amount of communication in decentralized target tracking. We focus on the scenario, where a team of robots is 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, which guarantees 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 decentralized strategies to reduce 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a particular robot should seek up-to-date information from its neighbors and when it is safe to operate with possibly outdated information. We prove that this strategy converges asymptotically to the desired formation when the target is stationary. For the case of a mobile target, we use a decentralized Kalman filter with covariance intersection to share the beliefs of neighboring robots. We evaluate all the approaches through simulations and a proof-of-concept experiment. 

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2. Distributed Simultaneous Action and Target Assignment for Multi-Robot Multi-Target Tracking

   https://doi.org/10.1109/ICRA.2018.8460974  

   Sung, Yoonchang; Budhiraja, Ashish Kumar; Williams, Ryan K.; Tokekar, Pratap (May 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA))

   We study two multi-robot assignment problems for multi-target tracking. We consider distributed approaches in order to deal with limited sensing and communication ranges. We seek to simultaneously assign trajectories and targets to the robots. Our focus is on \emph{local} algorithms that achieve performance close to the optimal algorithms with limited communication. We show how to use a local algorithm that guarantees a bounded approximate solution within $$\mathcal{O}(h\log{1/\epsilon})$$ communication rounds. We compare with a greedy approach that achieves a $$2$$--approximation in as many rounds as the number of robots. Simulation results show that the local algorithm is an effective solution to the assignment problem. 

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3. Opportunistic Multi-Robot Environmental Sampling via Decentralized Markov Decision Processes

   Ayan Dutta, O. Patrick (January 2021, International Symposium Distributed Autonomous Robotic Systems)

   We study the problem of information sampling with a group of mobile robots from an unknown environment. Each robot is given a unique region in the environment for the sampling task. The objective of the robots is to visit a subset of locations in the environment such that the collected information is maximized, and consequently, the underlying information model matches as close to reality as possible. The robots have limited communication ranges, and therefore can only communicate when nearby one another. The robots operate in a stochastic environment and their control uncertainty is handled using factored Decentralized Markov Decision Processes (Dec-MDP). When two or more robots communicate, they share their past noisy observations and use a Gaussian mixture model to update their local information models. This in turn helps them to obtain a better Dec-MDP policy. Simulation results show that our proposed strategy is able to predict the information model closer to the ground truth version than compared to other algorithms. Furthermore, the reduction in the overall uncertainty is more than comparable algorithms. 

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4. Opportunistic Multi-robot Environmental Sampling via Decentralized Markov Decision Processes

   https://doi.org/10.1007/978-3-030-92790-5_13  

   Dutta, Ayan; Patrick_Kreidl, O; O’Kane, Jason M (January 2022, Springer International Publishing)

   We study the problem of information sampling with a group of mobile robots from an unknown environment. Each robot is given a unique region in the environment for the sampling task. The objective of the robots is to visit a subset of locations in the environment such that the collected information is maximized, and consequently, the underlying information model matches as close to reality as possible. The robots have limited communication ranges, and therefore can only communicate when nearby one another. The robots operate in a stochastic environment and their control uncertainty is handled using factored Decentralized Markov Decision Processes (Dec-MDP). When two or more robots communicate, they share their past noisy observations and use a Gaussian mixture model to update their local information models. This in turn helps them to obtain a better Dec-MDP policy. Simulation results show that our proposed strategy is able to predict the information model closer to the ground truth version than compared to other algorithms. Furthermore, the reduction in the overall uncertainty is more than comparable algorithms. 

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5. Planning with Critical Decision Points: Robots that Influence Humans to Infer Their Strategy

   https://doi.org/10.1109/RO-MAN60168.2024.10731213  

   Ghose, Debasmita; Lewkowicz, Michal; Dong, David; Cheng, Andy; Doan, Tran; Adams, Emma; Vázquez, Marynel; Scassellati, Brian (August 2024, IEEE)

   To enable sophisticated interactions between humans and robots in a shared environment, robots must infer the intentions and strategies of their human counterparts. This inference can provide a competitive edge to the robot or enhance human-robot collaboration by reducing the necessity for explicit communication about task decisions. In this work, we identify specific states within the shared environment, which we refer to as Critical Decision Points, where the actions of a human would be especially indicative of their high-level strategy. A robot can significantly reduce uncertainty regarding the human’s strategy by observing actions at these points. To demonstrate the practical value of Critical Decision Points, we propose a Receding Horizon Planning (RHP) approach for the robot to influence the movement of a human opponent in a competitive game of hide-and-seek in a partially observable setting. The human plays as the hider and the robot plays as the seeker. We show that the seeker can influence the hider to move towards Critical Decision Points, and this can facilitate a more accurate estimation of the hider’s strategy. In turn, this helps the seeker catch the hider faster than estimating the hider’s strategy whenever the hider is visible or when the seeker only optimizes for minimizing its distance to the hider. 

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