Energy-Aware Planning-Scheduling for Autonomous Aerial Robots

TL;DR

This paper introduces an online planning and scheduling method for battery-powered aerial robots that optimizes coverage paths and computational tasks, enhancing fault tolerance and energy efficiency during flight.

Contribution

It presents a novel integrated planning-scheduling approach with a new variable coverage motion and an empirically validated energy model for aerial robots.

Findings

Improved fault tolerance in aerial robot flights

Adaptive flight plan adjustments based on battery status

Enhanced energy efficiency through integrated planning

Abstract

In this paper, we present an online planning-scheduling approach for battery-powered autonomous aerial robots. The approach consists of simultaneously planning a coverage path and scheduling onboard computational tasks. We further derive a novel variable coverage motion robust to airborne constraints and an empirically motivated energy model. The model includes the energy contribution of the schedule based on an automatic computational energy modeling tool. Our experiments show how an initial flight plan is adjusted online as a function of the available battery, accounting for uncertainty. Our approach remedies possible in-flight failure in case of unexpected battery drops, e.g., due to adverse atmospheric conditions, and increases the overall fault tolerance.