Recent advances in sensing, communication, and computing technologies have enabled the use of multirobot systems for practical applications such as surveillance, area mapping, and search and rescue. For such systems, a major challenge is to design decision rules that are real-time-implementable, require local information only, and guarantee some desired global performance. Distributed optimization provides a framework for designing such local decision-making rules for multirobot systems. In this article, we present a collection of selected results for distributed optimization for robot networks. We will focus on two special classes of problems: 1) real-time path planning for multirobot systems and 2) self-organization in multirobot systems using game-theoretic approaches. For multirobot path planning, we will present some recent approaches that are based on approximately solving distributed optimization problems over continuous and discrete domains of actions. The main idea underlying these approaches is that a variety of path planning problems can be formulated as convex optimization and submodular minimization problems over continuous and discrete action spaces, respectively. To generate local update rules that are efficiently implementable in real time, these approaches rely on approximate solutions to the global problems that can still guarantee some level of desired global performance. For game-theoretic self-organization, we will present a sampling of results for area coverage and real-time target assignment. In these results, the problems are formulated as games, and online updating rules are designed to enable teams of robots to achieve the collective objective in a distributed manner.

中文翻译：

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机器人网络的分布式优化：从实时凸优化到博弈论自组织

传感、通信和计算技术的最新进展使多机器人系统能够用于实际应用，例如监视、区域测绘以及搜索和救援。对于此类系统，一个主要挑战是设计可实时实施、仅需要本地信息并保证某些所需全局性能的决策规则。分布式优化为多机器人系统设计此类本地决策规则提供了一个框架。在本文中，我们展示了一组用于机器人网络分布式优化的选定结果。我们将关注两类特殊问题：1) 多机器人系统的实时路径规划和 2) 多机器人系统中使用博弈论方法的自组织。对于多机器人路径规划，我们将介绍一些最近的方法，这些方法基于近似解决连续和离散动作域上的分布式优化问题。这些方法背后的主要思想是，可以将各种路径规划问题分别表述为连续和离散动作空间上的凸优化和子模块最小化问题。为了生成可实时有效实现的本地更新规则，这些方法依赖于全局问题的近似解决方案，这些解决方案仍然可以保证一定水平的所需全局性能。对于博弈论自组织，我们将展示区域覆盖和实时目标分配的结果样本。在这些结果中，问题被表述为游戏，