Multi-Robot Pickup and Delivery via Distributed Resource Allocation
In this article, we consider a large-scale instance of the classical pickup-and-delivery vehicle routing problem that must be solved by a network of mobile cooperating robots. Robots must self-coordinate and self-allocate a set of pickup/delivery tasks while minimizing a given cost figure. This resu...
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| Published in: | IEEE transactions on robotics Vol. 39; no. 2; pp. 1 - 13 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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New York
IEEE
01.04.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1552-3098, 1941-0468 |
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| Abstract | In this article, we consider a large-scale instance of the classical pickup-and-delivery vehicle routing problem that must be solved by a network of mobile cooperating robots. Robots must self-coordinate and self-allocate a set of pickup/delivery tasks while minimizing a given cost figure. This results in a large, challenging mixed-integer linear problem that must be cooperatively solved without a central coordinator. We propose a distributed algorithm based on a primal decomposition approach that provides a feasible solution to the problem in finite time. An interesting feature of the proposed scheme is that each robot computes only its own block of solution, thereby preserving privacy of sensible information. The algorithm also exhibits attractive scalability properties that guarantee solvability of the problem even in large networks. To the best of our knowledge, this is the first attempt to provide a scalable distributed solution to the problem. The algorithm is first tested through Gazebo simulations on a ROS 2 platform, highlighting the effectiveness of the proposed solution. Finally, experiments on a real testbed with a team of ground and aerial robots are provided. |
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| AbstractList | In this article, we consider a large-scale instance of the classical pickup-and-delivery vehicle routing problem that must be solved by a network of mobile cooperating robots. Robots must self-coordinate and self-allocate a set of pickup/delivery tasks while minimizing a given cost figure. This results in a large, challenging mixed-integer linear problem that must be cooperatively solved without a central coordinator. We propose a distributed algorithm based on a primal decomposition approach that provides a feasible solution to the problem in finite time. An interesting feature of the proposed scheme is that each robot computes only its own block of solution, thereby preserving privacy of sensible information. The algorithm also exhibits attractive scalability properties that guarantee solvability of the problem even in large networks. To the best of our knowledge, this is the first attempt to provide a scalable distributed solution to the problem. The algorithm is first tested through Gazebo simulations on a ROS 2 platform, highlighting the effectiveness of the proposed solution. Finally, experiments on a real testbed with a team of ground and aerial robots are provided. In this article, we consider a large-scale instance of the classical pickup-and-delivery vehicle routing problem that must be solved by a network of mobile cooperating robots. Robots must self-coordinate and self-allocate a set of pickup/delivery tasks while minimizing a given cost figure. This results in a large, challenging mixed-integer linear problem that must be cooperatively solved without a central coordinator. We propose a distributed algorithm based on a primal decomposition approach that provides a feasible solution to the problem in finite time. An interesting feature of the proposed scheme is that each robot computes only its own block of solution, thereby preserving privacy of sensible information. The algorithm also exhibits attractive scalability properties that guarantee solvability of the problem even in large networks. To the best of our knowledge, this is the first attempt to provide a scalable distributed solution to the problem. The algorithm is first tested through Gazebo simulations on a ROS 2 platform, highlighting the effectiveness of the proposed solution. Finally, experiments on a real testbed with a team of ground and aerial robots are provided. |
| Author | Notarstefano, Giuseppe Camisa, Andrea Testa, Andrea |
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| Cites_doi | 10.1109/TRO.2018.2795034 10.1137/1.9780898718515 10.1016/j.ifacol.2020.12.382 10.1109/TRO.2017.2693377 10.1007/s11301-008-0036-4 10.1016/j.automatica.2012.06.040 10.1109/TASE.2019.2914113 10.1109/IROS.2004.1389727 10.1109/TAC.2009.2028954 10.1109/TASE.2015.2438032 10.1007/s11036-008-0101-1 10.1287/opre.1060.0283 10.1002/tee.21868 10.1109/TASE.2017.2767379 10.1109/CDC.2013.6760447 10.1561/2600000020 10.1007/978-3-642-40776-5_10 10.1109/ICRA.2014.6907709 10.1287/opre.20.1.58 10.1016/S0005-1098(98)00178-2 10.1109/TASE.2019.2952523 10.1109/LRA.2021.3061366 10.1109/ICAL.2011.6024686 10.1109/TAC.2019.2920812 10.1109/MCS.2019.2949973 10.2514/6.2019-0915 10.1109/TASE.2018.2879875 10.1109/TRO.2021.3120046 10.1109/TAC.2010.2092850 10.1109/JPROC.2011.2158181 10.1080/00207543.2015.1043403 10.1109/LCSYS.2018.2844353 10.1109/LRA.2019.2926966 10.1109/TAC.2021.3057061 10.1016/j.ejor.2012.08.015 10.1145/2245276.2245419 |
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| SubjectTerms | Algorithms Cooperating robots Costs distributed optimization distributed robot systems Heuristic algorithms Mixed integer Multiple robots Optimization planning Resource allocation Robot kinematics Robots Route planning scheduling and coordination Task analysis Vehicle dynamics Vehicle routing |
| Title | Multi-Robot Pickup and Delivery via Distributed Resource Allocation |
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