Distributed Optimization for Robot Networks: From Real-Time Convex Optimization to Game-Theoretic Self-Organization
Type
ArticleAuthors
Jaleel, HassanShamma, Jeff S.

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
RISC Laboratory
Date
2020-10-27Online Publication Date
2020-10-27Print Publication Date
2020-11Submitted Date
2019-11-22Permanent link to this record
http://hdl.handle.net/10754/665668
Metadata
Show full item recordAbstract
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.2020.3028295Publisher
IEEEJournal
Proceedings of the IEEEAdditional Links
https://ieeexplore.ieee.org/document/9241495/https://ieeexplore.ieee.org/document/9241495/
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9241495
ae974a485f413a2113503eed53cd6c53
10.1109/JPROC.2020.3028295