Distributed Optimization Methods for Multi-Robot Systems: Part II -- A Survey
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866909406723047424 |
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| author | Shorinwa, Ola Halsted, Trevor Yu, Javier Schwager, Mac |
| author_facet | Shorinwa, Ola Halsted, Trevor Yu, Javier Schwager, Mac |
| contents | Although the field of distributed optimization is well-developed, relevant literature focused on the application of distributed optimization to multi-robot problems is limited. This survey constitutes the second part of a two-part series on distributed optimization applied to multi-robot problems. In this paper, we survey three main classes of distributed optimization algorithms -- distributed first-order methods, distributed sequential convex programming methods, and alternating direction method of multipliers (ADMM) methods -- focusing on fully-distributed methods that do not require coordination or computation by a central computer. We describe the fundamental structure of each category and note important variations around this structure, designed to address its associated drawbacks. Further, we provide practical implications of noteworthy assumptions made by distributed optimization algorithms, noting the classes of robotics problems suitable for these algorithms. Moreover, we identify important open research challenges in distributed optimization, specifically for robotics problems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2301_11361 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Distributed Optimization Methods for Multi-Robot Systems: Part II -- A Survey Shorinwa, Ola Halsted, Trevor Yu, Javier Schwager, Mac Robotics Multiagent Systems Although the field of distributed optimization is well-developed, relevant literature focused on the application of distributed optimization to multi-robot problems is limited. This survey constitutes the second part of a two-part series on distributed optimization applied to multi-robot problems. In this paper, we survey three main classes of distributed optimization algorithms -- distributed first-order methods, distributed sequential convex programming methods, and alternating direction method of multipliers (ADMM) methods -- focusing on fully-distributed methods that do not require coordination or computation by a central computer. We describe the fundamental structure of each category and note important variations around this structure, designed to address its associated drawbacks. Further, we provide practical implications of noteworthy assumptions made by distributed optimization algorithms, noting the classes of robotics problems suitable for these algorithms. Moreover, we identify important open research challenges in distributed optimization, specifically for robotics problems. |
| title | Distributed Optimization Methods for Multi-Robot Systems: Part II -- A Survey |
| topic | Robotics Multiagent Systems |
| url | https://arxiv.org/abs/2301.11361 |