Pareto Control Barrier Function for Inner Safe Set Maximization Under Input Constraints

TL;DR

This paper presents the Pareto Control Barrier Function (PCBF) algorithm that maximizes the safe set volume for dynamical systems under input constraints, balancing safety and performance efficiently.

Contribution

The paper introduces a novel PCBF algorithm that leverages Pareto multi-task learning to enhance safe set maximization under input constraints, applicable to high-dimensional systems.

Findings

PCBF outperforms Hamilton-Jacobi reachability in safe set size.

Effective in high-dimensional systems like a 12D quadrotor.

Ensures safety while respecting input constraints.

Abstract

This article introduces the Pareto Control Barrier Function (PCBF) algorithm to maximize the inner safe set of dynamical systems under input constraints. Traditional Control Barrier Functions (CBFs) ensure safety by maintaining system trajectories within a safe set but often fail to account for realistic input constraints. To address this problem, we leverage the Pareto multi-task learning framework to balance competing objectives of safety and safe set volume. The PCBF algorithm is applicable to high-dimensional systems and is computationally efficient. We validate its effectiveness through comparison with Hamilton-Jacobi reachability for an inverted pendulum and through simulations on a 12-dimensional quadrotor system. Results show that the PCBF consistently outperforms existing methods, yielding larger safe sets and ensuring safety under input constraints.