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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Veröffentlicht in:	IEEE transactions on robotics Jg. 39; H. 2; S. 1 - 13
Hauptverfasser:	Camisa, Andrea, Testa, Andrea, Notarstefano, Giuseppe
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
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.
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
Author_xml	– sequence: 1 givenname: Andrea orcidid: 0000-0001-8524-2540 surname: Camisa fullname: Camisa, Andrea organization: Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy – sequence: 2 givenname: Andrea orcidid: 0000-0002-8118-5320 surname: Testa fullname: Testa, Andrea organization: Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy – sequence: 3 givenname: Giuseppe orcidid: 0000-0002-0906-7848 surname: Notarstefano fullname: Notarstefano, Giuseppe organization: Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy
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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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