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.

翻译：电池驱动机器人系统中的能量充足性维护对于长期任务至关重要。该能力需具备足够灵活性以适应不同类型环境和广泛任务需求，同时持续保障机器人不会耗尽能量。本研究提出基于控制障碍函数（CBFs）的框架，提供可应用于任意路径规划器之上的能量充足性保障层，能够在任务执行期间为机器人能耗提供理论保证。实践中，我们采用双Sigmoid函数对通用路径规划器输出进行平滑处理，进而利用CBFs确保沿平滑路径的能量充足性，适用于单积分器和独轮车运动学模型的机器人。我们通过基于物理的机器人仿真器以及搭载完整定位与建图栈的真实机器人开展实验，验证了该方法的有效性。