Least Restrictive Hyperplane Control Barrier Functions

TL;DR

This paper introduces Least Restrictive Hyperplane Control Barrier Functions (H-CBFs), optimizing hyperplane orientation to provide less conservative safety guarantees for dynamic systems near complex obstacles.

Contribution

It proposes a novel method to optimize hyperplane orientation in H-CBFs, reducing conservativeness and improving control performance near obstacles.

Findings

Less conservative safety guarantees achieved

Enhanced control flexibility near obstacles

Validated on a double integrator system with complex obstacles

Abstract

Control Barrier Functions (CBFs) can provide provable safety guarantees for dynamic systems. However, finding a valid CBF for a system of interest is often non-trivial, especially for systems having low computational resources, higher-order dynamics, and moving close to obstacles of complex shape. A common solution to this problem is to use a purely distance-based CBF. In this paper, we study Hyperplane CBFs (H-CBFs), where a hyperplane separates the agent from the obstacle. First, we note that the common distance-based CBF is a special case of an H-CBF where the hyperplane is a supporting hyperplane of the obstacle that is orthogonal to a line between the agent and the obstacle. Then we show that a less conservative CBF can be found by optimising over the orientation of the supporting hyperplane, in order to find the Least Restrictive Hyperplane CBF. This enables us to maintain the safety guarantees while allowing controls that are closer to the desired ones, especially when moving fast and passing close to obstacles. We illustrate the approach on a double integrator dynamical system with acceleration constraints, moving through a group of arbitrarily shaped static and moving obstacles.