Guaranteed-Safe MPPI Through Composite Control Barrier Functions for Efficient Sampling in Multi-Constrained Robotic Systems

Fuente: arXiv
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Hauptverfasser: Rabiee, Pedram, Hoagg, Jesse B.
Format: Preprint
Veröffentlicht: 2024
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author Rabiee, Pedram
Hoagg, Jesse B.
author_facet Rabiee, Pedram
Hoagg, Jesse B.
contents We present a new guaranteed-safe model predictive path integral (GS-MPPI) control algorithm that enhances sample efficiency in nonlinear systems with multiple safety constraints. The approach use a composite control barrier function (CBF) along with MPPI to ensure all sampled trajectories are provably safe. We first construct a single CBF constraint from multiple safety constraints with potentially differing relative degrees, using it to create a safe closed-form control law. This safe control is then integrated into the system dynamics, allowing MPPI to optimize over exclusively safe trajectories. The method not only improves computational efficiency but also addresses the myopic behavior often associated with CBFs by incorporating long-term performance considerations. We demonstrate the algorithm's effectiveness through simulations of a nonholonomic ground robot subject to position and speed constraints, showcasing safety and performance.
format Preprint
id arxiv_https___arxiv_org_abs_2410_02154
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Guaranteed-Safe MPPI Through Composite Control Barrier Functions for Efficient Sampling in Multi-Constrained Robotic Systems
Rabiee, Pedram
Hoagg, Jesse B.
Systems and Control
We present a new guaranteed-safe model predictive path integral (GS-MPPI) control algorithm that enhances sample efficiency in nonlinear systems with multiple safety constraints. The approach use a composite control barrier function (CBF) along with MPPI to ensure all sampled trajectories are provably safe. We first construct a single CBF constraint from multiple safety constraints with potentially differing relative degrees, using it to create a safe closed-form control law. This safe control is then integrated into the system dynamics, allowing MPPI to optimize over exclusively safe trajectories. The method not only improves computational efficiency but also addresses the myopic behavior often associated with CBFs by incorporating long-term performance considerations. We demonstrate the algorithm's effectiveness through simulations of a nonholonomic ground robot subject to position and speed constraints, showcasing safety and performance.
title Guaranteed-Safe MPPI Through Composite Control Barrier Functions for Efficient Sampling in Multi-Constrained Robotic Systems
topic Systems and Control
url https://arxiv.org/abs/2410.02154