Energy-Aware Informative Path Planning for Heterogeneous Multi-Robot Systems

Abstract

Effective energy management is essential for maximizing information gathering tasks with networked mobile robots, particularly for large-scale, energy-intensive tasks such as agricultural monitoring and wildfire mapping. This paper presents a novel framework that integrates robots’ energy profiles with confidence bounds of their assigned regions to optimize sampling targets. Designed for persistent, long-term deployments, the framework employs Gaussian Process Regression (GPR) to maximize data acquisition and accurately reconstruct unknown spatial distributions (e.g., algae outbreaks or humidity maps). The method enables seamless transitions among exploration (mapping uncertain regions at high energy), exploitation (refining maps at moderate energy levels), and recharging (navigating to charging stations at low energy), thereby achieving energy-balanced informative path planning. Experiments demonstrate the effectiveness of the approach against state-of-the-art methods in generating energy-efficient and distinct paths for heterogeneous robots, delivering up to 32% energy savings while maintaining high reconstruction accuracy. Hardware experiments closely matched the performance in simulation.