Safe Navigation in Unmapped Environments for Robotic Systems with Input Constraints

TL;DR

This paper introduces a novel control method for robotic navigation in unmapped environments that ensures safety and input constraints are satisfied in real-time using composite control barrier functions and local perception data.

Contribution

It develops a new approach combining local perception-based CBFs with input constraints, enabling safe navigation in unknown environments with real-time updates.

Findings

Successful simulation of a ground robot navigating unmapped environments

The method ensures obstacle avoidance and input constraint satisfaction

Real-time local perception effectively guides safe navigation

Abstract

This paper presents an approach for navigation and control in unmapped environments under input and state constraints using a composite control barrier function (CBF). We consider the scenario where real-time perception feedback (e.g., LiDAR) is used online to construct a local CBF that models local state constraints (e.g., local safety constraints such as obstacles) in the a priori unmapped environment. The approach employs a soft-maximum function to synthesize a single time-varying CBF from the N most recently obtained local CBFs. Next, the input constraints are transformed into controller-state constraints through the use of control dynamics. Then, we use a soft-minimum function to compose the input constraints with the time-varying CBF that models the a priori unmapped environment. This composition yields a single relaxed CBF, which is used in a constrained optimization to obtain an optimal control that satisfies the state and input constraints. The approach is validated through simulations of a nonholonomic ground robot that is equipped with LiDAR and navigates an unmapped environment. The robot successfully navigates the environment while avoiding the a priori unmapped obstacles and satisfying both speed and input constraints.