Heterogeneity-Aware Graph Partitioning for Distributed Deployment of Multiagent Systems
Abstract
In this work, we examine the distributed coverage control problem for deploying a team of heterogeneous robots with nonlinear dynamics in a partially known environment modeled as a weighted mixed graph. By defining an optimal tracking control problem, using a discounted cost function and state-dependent Riccati equation (SDRE) approach, a new partitioning algorithm is proposed to capture the heterogeneity in robots dynamics. The considered partitioning cost, which is a state-dependent proximity metric, penalizes both the tracking error and the control input energy that occurs during the movement of a robot, on a straight line, to an arbitrary node of the graph in a predefined finite time. We show that the size of the subgraph associated with each robot depends on its resources and capabilities in comparison to its neighbors. Also, a distributed deployment strategy is proposed to optimally distribute robots aiming at persistently monitoring specified regions of interest. Finally, a series of simulations and experimental studies is carried out to demonstrate the viability and efficacy of the proposed methodology in deploying heterogeneous multiagent systems.
Publication Title
IEEE Transactions on Cybernetics
Recommended Citation
Davoodi, M., & Velni, J. (2022). Heterogeneity-Aware Graph Partitioning for Distributed Deployment of Multiagent Systems. IEEE Transactions on Cybernetics, 52 (4), 2578-2588. https://doi.org/10.1109/TCYB.2020.3005116
