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

Type
Article

Authors
Jaleel, Hassan
Shamma, Jeff S.

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
RISC Laboratory

Online Publication Date
2020-10-27

Print Publication Date
2020-11

Date
2020-10-27

Submitted Date
2019-11-22

Abstract
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.

Citation
Jaleel, H., & Shamma, J. S. (2020). Distributed Optimization for Robot Networks: From Real-Time Convex Optimization to Game-Theoretic Self-Organization. Proceedings of the IEEE, 108(11), 1953–1967. doi:10.1109/jproc.2020.3028295

Publisher
Institute of Electrical and Electronics Engineers (IEEE)

Journal
Proceedings of the IEEE

DOI
10.1109/JPROC.2020.3028295

Additional Links
https://ieeexplore.ieee.org/document/9241495/https://ieeexplore.ieee.org/document/9241495/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9241495

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