Safe Robot Control using Occupancy Grid Map-based Control Barrier Function (OGM-CBF)

TL;DR

This paper introduces a novel control barrier function method that integrates occupancy grid maps and signed distance functions to ensure safe robot navigation in unknown environments, adaptable to various sensors and obstacle shapes.

Contribution

It presents a new CBF construction from perception data that handles arbitrary obstacle shapes and integrates seamlessly with sensor inputs for real-time safety guarantees.

Findings

Effective in simulation with autonomous driving scenarios

Validated with real-world industrial robot experiments

Handles complex obstacle shapes in real-time

Abstract

Safe control in unknown environments is a significant challenge in robotics. While Control Barrier Functions (CBFs) are widely used to guarantee system safety, they often assume known environments with predefined obstacles. The proposed method constructs CBFs directly from perception sensor input and introduces a new first-order barrier function for a 3D kinematic robot motion model. The proposed CBF is constructed by combining Occupancy Grid Mapping (OGM) and Signed Distance Functions (SDF). The OGM framework abstracts sensor inputs, making the solution compatible with any sensor modality capable of generating occupancy maps. Moreover, the OGM enhances situational awareness along the robot's motion trajectory, by integrating both current and previously mapped data. The SDF encapsulates complex obstacle shapes defined by OGM into real-time computable values, enabling the method to handle obstacles of arbitrary shapes. This enables a single constraint in the CBF-QP optimization for each point on the robot, regardless of the number or shape of obstacles. The effectiveness of the proposed approach is demonstrated through simulations on autonomous driving in the CARLA simulator and real-world experiments with an industrial mobile robot, using a simplified 2D version of the method.