Multi-Robot Localization and Target Tracking with Connectivity Maintenance and Collision Avoidance

TL;DR

This paper presents a two-stage approach for multi-robot localization and target tracking that maintains connectivity and avoids collisions, using a greedy algorithm and control barrier functions, validated through simulations.

Contribution

It introduces a novel two-stage method combining greedy optimization and control barrier functions for safe, efficient multi-robot localization and tracking.

Findings

The greedy algorithm achieves high task quality.

It runs more efficiently than nonlinear optimization solvers.

Simulation results verify effectiveness and safety.

Abstract

We study the problem that requires a team of robots to perform joint localization and target tracking task while ensuring team connectivity and collision avoidance. The problem can be formalized as a nonlinear, non-convex optimization program, which is typically hard to solve. To this end, we design a two-staged approach that utilizes a greedy algorithm to optimize the joint localization and target tracking performance and applies control barrier functions to ensure safety constraints, i.e., maintaining connectivity of the robot team and preventing inter-robot collisions. Simulated Gazebo experiments verify the effectiveness of the proposed approach. We further compare our greedy algorithm to a non-linear optimization solver and a random algorithm, in terms of the joint localization and tracking quality as well as the computation time. The results demonstrate that our greedy algorithm achieves high task quality and runs efficiently.