Distributed Perception Aware Safe Leader Follower System via Control Barrier Methods

TL;DR

This paper presents a distributed control framework for multi-agent leader-follower systems using body-fixed cameras, incorporating control barrier functions to maintain visibility constraints and neural network estimators for real-time state estimation.

Contribution

It introduces a perception-aware control scheme with FOV constraints as state limits, utilizing CBF-based quadratic programs and novel neural network estimators for improved robustness.

Findings

Effective in maintaining leader visibility in simulations

Robust state estimation from real-time image data

Demonstrated superior performance in Gazebo environments

Abstract

This paper addresses a distributed leader-follower formation control problem for a group of agents, each using a body-fixed camera with a limited field of view (FOV) for state estimation. The main challenge arises from the need to coordinate the agents' movements with their cameras' FOV to maintain visibility of the leader for accurate and reliable state estimation. To address this challenge, we propose a novel perception-aware distributed leader-follower safe control scheme that incorporates FOV limits as state constraints. A Control Barrier Function (CBF) based quadratic program is employed to ensure the forward invariance of a safety set defined by these constraints. Furthermore, new neural network based and double bounding boxes based estimators, combined with temporal filters, are developed to estimate system states directly from real-time image data, providing consistent performance across various environments. Comparison results in the Gazebo simulator demonstrate the effectiveness and robustness of the proposed framework in two distinct environments.