Safe Navigation in Unmapped Environments for Robotic Systems with Input Constraints

2024-10-03

arXiv.org

AI Summary

This work addresses safe navigation for robots operating in unknown environments under coupled input and state constraints. We propose a real-time, map-free safety-critical control framework that constructs local Control Barrier Functions (CBFs) online from LiDAR measurements. Our method introduces a soft-max operator to dynamically fuse time-varying local CBFs across multiple frames and employs a soft-min operator to embed actuator input constraints—such as velocity limits and saturation bounds—into controller-state constraints, yielding a relaxed CBF-driven convex optimization controller. Key contributions are: (1) the first differentiable, unified, and time-varying integration of local CBFs with hard input constraints via soft-extremum operators; and (2) complete reliance on real-time perception without environment mapping or global prior knowledge. In simulations with nonholonomic ground robots, the approach achieves 100% unknown obstacle avoidance while strictly respecting all state and input constraints, demonstrating online co-guarantee of safety and feasibility.

Technology Category

• Intelligent Robots: State Estimation
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Safe navigation for robots in unmapped environments with constraints
• Real-time perception feedback to model local safety constraints
• Optimal control synthesis satisfying state and input constraints

Innovation

• Composite control barrier function for safe navigation
• Soft-maximum synthesizes time-varying CBF from local feedback
• Soft-minimum combines input constraints with CBF