Distributed Optimization for Robot Networks: From Real-Time Convex Optimization to Game-Theoretic Self-Organization

Abstract

Recent advances in sensing, communication, and computing technologies have enabled the use of multi-robot systems for practical applications like 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 multi-robot systems. In this tutorial, we present a collection of selected results for distributed optimization for robot networks. We will focus on two special classes of problems: (i) real-time path planning for multi-robot systems and (ii) self-organization in multi-robot 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.

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