Learning Spatiotemporal Tubes for Temporal Reach-Avoid-Stay Tasks using Physics-Informed Neural Networks

TL;DR

This paper introduces a control framework using Physics-Informed Neural Networks to learn spatiotemporal tubes for ensuring systems meet reach-avoid-stay tasks amid uncertainties and disturbances.

Contribution

It proposes a novel STT-based control method with a PINN to approximate time-varying safety regions and a verification scheme for continuous-time guarantees.

Findings

Successfully applied to mobile robot navigation in cluttered environments.

Demonstrated effectiveness in aerial vehicle obstacle avoidance.

Validated scalability and robustness of the approach.

Abstract

This paper presents a Spatiotemporal Tube (STT)-based control framework for general control-affine MIMO nonlinear pure-feedback systems with unknown dynamics to satisfy prescribed time reach-avoid-stay tasks under external disturbances. The STT is defined as a time-varying ball, whose center and radius are jointly approximated by a Physics-Informed Neural Network (PINN). The constraints governing the STT are first formulated as loss functions of the PINN, and a training algorithm is proposed to minimize the overall violation. The PINN being trained on certain collocation points, we propose a Lipschitz-based validity condition to formally verify that the learned PINN satisfies the conditions over the continuous time horizon. Building on the learned STT representation, an approximation-free closed-form controller is defined to guarantee satisfaction of the T-RAS specification. Finally, the effectiveness and scalability of the framework are validated through two case studies involving a mobile robot and an aerial vehicle navigating through cluttered environments.