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Lin X, Yazcoglu Y, Aksaray D. Robust Planning for Persistent Surveillance With Energy-Constrained UAVs and Mobile Charging Stations. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3146938] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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2
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A Framework for Multi-UAV Persistent Search and Retrieval with Stochastic Target Appearance in a Continuous Space. J INTELL ROBOT SYST 2021. [DOI: 10.1007/s10846-021-01484-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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3
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Lien JM, Rodriguez S, Morales M. Persistent Covering With Latency and Energy Constraints. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3056381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Hari SKK, Rathinam S, Darbha S, Kalyanam K, Manyam SG, Casbeer D. Optimal UAV Route Planning for Persistent Monitoring Missions. IEEE T ROBOT 2021. [DOI: 10.1109/tro.2020.3032171] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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5
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Scherer J, Rinner B. Multi-UAV Surveillance With Minimum Information Idleness and Latency Constraints. IEEE Robot Autom Lett 2020. [DOI: 10.1109/lra.2020.3003884] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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6
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Palacios-Gasós JM, Tardioli D, Montijano E, Sagüés C. Equitable persistent coverage of non-convex environments with graph-based planning. Int J Rob Res 2019. [DOI: 10.1177/0278364919882082] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this article, we tackle the problem of persistently covering a complex non-convex environment with a team of robots. We consider scenarios where the coverage quality of the environment deteriorates with time, requiring every point to be constantly revisited. As a first step, our solution finds a partition of the environment where the amount of work for each robot, weighted by the importance of each point, is equal. This is achieved using a power diagram and finding an equitable partition through a provably correct distributed control law on the power weights. Compared with other existing partitioning methods, our solution considers a continuous environment formulation with non-convex obstacles. In the second step, each robot computes a graph that gathers sweep-like paths and covers its entire partition. At each planning time, the coverage error at the graph vertices is assigned as weights of the corresponding edges. Then, our solution is capable of efficiently finding the optimal open coverage path through the graph with respect to the coverage error per distance traversed. Simulation and experimental results are presented to support our proposal.
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Affiliation(s)
- José Manuel Palacios-Gasós
- Firmware Group, Induction Technology, Product Division Cookers, BSH Home Appliances Group, Zaragoza, Spain
| | - Danilo Tardioli
- Centro Universitario de la Defensa, Zaragoza, Spain
- Instituto de Investigación en Ingeniera de Aragón (I3A), Universidad de Zaragoza, Zaragoza, Spain
| | - Eduardo Montijano
- Instituto de Investigación en Ingeniera de Aragón (I3A), Universidad de Zaragoza, Zaragoza, Spain
| | - Carlos Sagüés
- Instituto de Investigación en Ingeniera de Aragón (I3A), Universidad de Zaragoza, Zaragoza, Spain
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Mitra A, Richards JA, Bagchi S, Sundaram S. Resilient distributed state estimation with mobile agents: overcoming Byzantine adversaries, communication losses, and intermittent measurements. Auton Robots 2018. [DOI: 10.1007/s10514-018-9813-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Best G, Cliff OM, Patten T, Mettu RR, Fitch R. Dec-MCTS: Decentralized planning for multi-robot active perception. Int J Rob Res 2018. [DOI: 10.1177/0278364918755924] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
We propose a decentralized variant of Monte Carlo tree search (MCTS) that is suitable for a variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimize its own actions by maintaining a probability distribution over plans in the joint-action space. Robots periodically communicate a compressed form of their search trees, which are used to update the joint distribution using a distributed optimization approach inspired by variational methods. Our method admits any objective function defined over robot action sequences, assumes intermittent communication, is anytime, and is suitable for online replanning. Our algorithm features a new MCTS tree expansion policy that is designed for our planning scenario. We extend the theoretical analysis of standard MCTS to provide guarantees for convergence rates to the optimal payoff sequence. We evaluate the performance of our method for generalized team orienteering and online active object recognition using real data, and show that it compares favorably to centralized MCTS even with severely degraded communication. These examples demonstrate the suitability of our algorithm for real-world active perception with multiple robots.
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Affiliation(s)
- Graeme Best
- Australian Centre for Field Robotics (ACFR), The University of Sydney, Sydney, Australia
| | - Oliver M Cliff
- Australian Centre for Field Robotics (ACFR), The University of Sydney, Sydney, Australia
| | - Timothy Patten
- Australian Centre for Field Robotics (ACFR), The University of Sydney, Sydney, Australia
- Automation and Control Institute, Vienna University of Technology, Vienna, Austria
| | - Ramgopal R Mettu
- Department of Computer Science, Tulane University, New Orleans, LA, USA
| | - Robert Fitch
- Australian Centre for Field Robotics (ACFR), The University of Sydney, Sydney, Australia
- Centre for Autonomous Systems, University of Technology Sydney, Sydney, Australia
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Ramasamy M, Ghose D. A Heuristic Learning Algorithm for Preferential Area Surveillance by Unmanned Aerial Vehicles. J INTELL ROBOT SYST 2017. [DOI: 10.1007/s10846-017-0498-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Palacios-Gasos JM, Montijano E, Sagues C, Llorente S. Distributed Coverage Estimation and Control for Multirobot Persistent Tasks. IEEE T ROBOT 2016. [DOI: 10.1109/tro.2016.2602383] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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11
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Yu J, Schwager M, Rus D. Correlated Orienteering Problem and its Application to Persistent Monitoring Tasks. IEEE T ROBOT 2016. [DOI: 10.1109/tro.2016.2593450] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Lan X, Schwager M. Rapidly Exploring Random Cycles: Persistent Estimation of Spatiotemporal Fields With Multiple Sensing Robots. IEEE T ROBOT 2016. [DOI: 10.1109/tro.2016.2596772] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Yu J, Karaman S, Rus D. Persistent Monitoring of Events With Stochastic Arrivals at Multiple Stations. IEEE T ROBOT 2015. [DOI: 10.1109/tro.2015.2409453] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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