Es-cbf: an energy sufficiency extension for sample based path planners to enable long term autonomy

Abstract

Maintaining energy sufficiency of a battery-powered robot system is essential for long-term missions. This capability should be flexible enough to deal with different types of environments and a wide range of missions, while constantly guaranteeing that the robot does not run out of energy. We present a framework based on Control Barrier Functions (CBFs) which provides an energy sufficiency layer that can be applied on a wide range of sample based path planners and provides guarantees on sufficiency of robot’s energy during mission execution. In practice, we smooth the output of an arbitrary path planner (i.e. a set of waypoints) 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.