Abstract
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 problem.
Abstract (translated)
尽管分布式优化领域已经得到了快速发展,但专注于将分布式优化应用于多机器人问题的相关文献有限。本调查是分布式优化应用于多机器人问题的第二个部分,旨在关注不需要中央计算机协调或计算的完全分布式方法。本文描述了每个类别的基本结构,并在此结构周围注意到重要的变异,旨在解决其相关的缺点。此外,我们提供了分布式优化算法的重要假设的实际影响,并注意到适用于这些算法的机器人问题类别。此外,我们还识别了分布式优化领域中重要的开放研究挑战,特别是针对机器人问题的研究挑战。
URL
https://arxiv.org/abs/2301.11361
