Generalization of Safe Optimal Control Actions on Networked Multi-Agent Systems

TL;DR

This paper introduces a unified framework for quickly generating safe, optimal control actions for new tasks in multi-agent systems by composing existing controllers with control barrier functions to ensure safety.

Contribution

It presents a novel method that combines existing controllers through weighted mixtures and guarantees safety using control barrier functions, avoiding extensive sampling.

Findings

Successfully applied to UAVs in obstacle environments

Enables rapid adaptation to new tasks without extensive tuning

Ensures safety through control barrier functions

Abstract

We propose a unified framework to fast generate a safe optimal control action for a new task from existing controllers on Multi-Agent Systems (MASs). The control action composition is achieved by taking a weighted mixture of the existing controllers according to the contribution of each component task. Instead of sophisticatedly tuning the cost parameters and other hyper-parameters for safe and reliable behavior in the optimal control framework, the safety of each single task solution is guaranteed using the control barrier functions (CBFs) for high-degree stochastic systems, which constrains the system state within a known safe operation region where it originates from. Linearity of CBF constraints in control enables the control action composition. The discussed framework can immediately provide reliable solutions to new tasks by taking a weighted mixture of solved component-task actions and filtering on some CBF constraints, instead of performing an extensive sampling to achieve a new controller. Our results are verified and demonstrated on both a single UAV and two cooperative UAV teams in an environment with obstacles.