Model Predictive Path Integral Methods with Reach-Avoid Tasks and Control Barrier Functions

TL;DR

This paper introduces an extension to the CBFkit toolbox that integrates reach-avoid planning with MPPI controllers, enabling safe, reliable robotic control in uncertain environments with formal safety guarantees.

Contribution

It presents a novel integration of reach-avoid specifications with MPPI controllers in CBFkit, enhancing safety and task satisfaction in robotics applications.

Findings

Successfully demonstrated in various robotic scenarios.

Achieves formal safety guarantees under uncertainty.

Optimized for speed using JAX and jaxopt.

Abstract

The rapid advancement of robotics necessitates robust tools for developing and testing safe control architectures in dynamic and uncertain environments. Ensuring safety and reliability in robotics, especially in safety-critical applications, is crucial, driving substantial industrial and academic efforts. In this context, we extend CBFkit, a Python/ROS2 toolbox, which now incorporates a planner using reach-avoid specifications as a cost function. This integration with the Model Predictive Path Integral (MPPI) controllers enables the toolbox to satisfy complex tasks while ensuring formal safety guarantees under various sources of uncertainty using Control Barrier Functions (CBFs). CBFkit is optimized for speed using JAX for automatic differentiation and jaxopt for quadratic program solving. The toolbox supports various robotic applications, including autonomous navigation, human-robot interaction, and multi-robot coordination. The toolbox also offers a comprehensive library of planner, controller, sensor, and estimator implementations. Through a series of examples, we demonstrate the enhanced capabilities of CBFkit in different robotic scenarios.