Energy Sufficiency in Unknown Environments via Control Barrier Functions

TL;DR

This paper introduces a control barrier function-based framework that guarantees energy sufficiency for robots in unknown environments, adaptable to various path planners and robot types, validated through simulations and real-world experiments.

Contribution

It proposes a novel energy sufficiency layer using CBFs that can be integrated with any path planner for long-term robot missions.

Findings

Framework guarantees energy sufficiency during missions

Effective with both simulated and real robots

Compatible with different robot kinematics

Abstract

Maintaining energy sufficiency of a battery-powered robot system is a essential for long-term missions. This capability should be flexible enough to deal with different types of environment and a wide range of missions, while constantly guaranteeing that the robot does not run out of energy. In this work we present a framework based on Control Barrier Functions (CBFs) that provides an energy sufficiency layer that can be applied on top of any path planner and provides guarantees on the robot's energy consumption during mission execution. In practice, we smooth the output of a generic path planner using double sigmoid functions and then use CBFs to ensure energy sufficiency along the smoothed path, for robots described by single integrator and unicycle kinematics. We present results using a physics-based robot simulator, as well as with real robots with a full localization and mapping stack to show the validity of our approach.