Distributed coordination in multi-agent systems: a graph Laplacian perspective

Abstract

This paper reviews some main results and progress in distributed multi-agent coordination from a graph Laplacian perspective. Distributed multi-agent coordination has been a very active subject studied extensively by the systems and control community in last decades, including distributed consensus, formation control, sensor localization, distributed optimization, etc. The aim of this paper is to provide both a comprehensive survey of existing literature in distributed multi-agent coordination and a new perspective in terms of graph Laplacian to categorize the fundamental mechanisms for distributed coordination. For different types of graph Laplacians, we summarize their inherent coordination features and specific research issues. This paper also highlights several promising research directions along with some open problems that are deemed important for future study.

Social Graphs and Their Applications to Robotics

“…The controlling problem similar to that of a mobile robot team, can be divided into two key parts: (1) optimal global mission decomposition into sub-missions and distributing it optimally among separate robots in the team; (2) path planning, control, and movement correction for each mobile robot [10]. In computer science, the research for multi-agent systems usually uses software agents that have been widely studied in the 1980s and 1990s [11]. Also, multi-agent systems have replaced single agents as the computing paradigm in artificial intelligence [11] [12].…”

“…In computer science, the research for multi-agent systems usually uses software agents that have been widely studied in the 1980s and 1990s [11]. Also, multi-agent systems have replaced single agents as the computing paradigm in artificial intelligence [11] [12]. In the robotic society, the agents in a multi-agent system can also be robots, thus multi-agent systems are referred to as multi-robot systems as well [11].…”

“…Also, multi-agent systems have replaced single agents as the computing paradigm in artificial intelligence [11] [12]. In the robotic society, the agents in a multi-agent system can also be robots, thus multi-agent systems are referred to as multi-robot systems as well [11]. In [13] the authors have introduced a taxonomy that classifies multi-agent robotic systems based on their computational abilities and communication techniques that has led to the understanding of team behaviour, which emerges collaboratively, and have described theoretical issues that may be raised in the study of cooperation of multi-robot systems, illustrating the usefulness of the taxonomy in simplifying discourse about robot collective properties.…”

“…Each one of these environments has specific features and drawbacks. A graph is considered a suitable model for motion planning problems and path planning purposes, as a basis, the graph model contains only possible paths, i.e., information about obstacles is excluded when the graph is established [11]. Masehian, E., & Sedighizadeh, D, (2007) mentioned that more than 50 percent of all recent robot planning algorithms are dependent on classical methods.…”

“…Visibility graphs are considered to be one of the oldest roadmap methods that applied in a 2D environment, and they were used on robot Shakey [35]. VG considers obstacle vertices in the environment to be the vertices, through which the robots can arrive at their required locations (or it is the set of lines in the free space that links an advantage of object to another; these advantages are vertices of polygonal obstacles [11][33] [34]). Visibility graph approaches proceed to link vertices that are visible to each other.…”