Connectivity Maintenance for Multi-Robot Systems Under Motion and Sensing Uncertainties Using Distributed ADMM-based Trajectory Planning

TL;DR

This paper introduces a distributed ADMM-based trajectory planning method that explicitly accounts for motion and sensing uncertainties to maintain connectivity in multi-robot systems.

Contribution

It presents a novel trajectory planner that incorporates uncertainties into the connectivity maintenance problem using a distributed optimization framework.

Findings

Successfully maintains connectivity above a threshold in simulations.

Reduces computational load via Hessian approximation.

Validates effectiveness through statistical simulation results.

Abstract

Inter-robot communication enables multi-robot systems to coordinate and execute complex missions efficiently. Thus, maintaining connectivity of the communication network between robots is essential for many multi-robot systems. In this paper, we present a trajectory planner for connectivity maintenance of a multi-robot system. We first define a weighted undirected graph to represent the connectivity of the system. Unlike previous connectivity maintenance works, we explicitly account for robot motion and sensing uncertainties while formulating the graph edge weights. These uncertainties result in uncertain robot positions which directly affect the connectivity of the system. Next, the algebraic connectivity of the weighted undirected graph is maintained above a specified lower limit using a trajectory planner based on a distributed alternating direction method of multipliers (ADMM) framework. Here we derive an approximation for the Hessian matrices required within the ADMM optimization step to reduce the computational load. Finally, simulation results are presented to statistically validate the connectivity maintenance of our trajectory planner.