Expected Free Energy-based Informative Path Planning for Robotic Mars Exploration

2026-08-14

AI Summary

This study addresses the challenge of balancing informative mapping and value-driven search under resource constraints in Mars exploration. We propose a unified path planning framework based on expected free energy, which serves as a single objective to simultaneously optimize both goals under Gaussian process beliefs while satisfying hard budget constraints through continuous trajectory optimization. Experimental results demonstrate that this framework significantly outperforms conventional information-theoretic baselines under comparable conditions. It achieves high-fidelity posterior map construction while accurately identifying high-value regions, effectively overcoming the limitations of single-objective optimization and enhancing overall autonomous exploration efficiency.

Abstract

An autonomous robot efficiently exploring an unknown environment, such as looking for water sources on Mars, faces two simultaneous demands: building an accurate information map while quickly finding the regions of greatest value, and paying for every meter of travel and the cost of every measurement it takes. Classical information-seeking and reward-seeking criteria address only one of these objectives at a time. Here, we propose Expected Free Energy (EFE), the principled action-selection objective from active inference, as a unifying criterion for budgeted robotic informative path planning. Maintaining a Gaussian-process belief over the information field, our agent plans continuous trajectories that minimize expected free energy under hard path-length constraints. The results from multiple realizations show that EFE-based planning yields accurate posterior maps and locates the highest-value regions simultaneously, outperforming information-theoretic baselines under the same settings. In robotic exploration, these unified, easy-to-tune principled information-gathering strategies facilitate autonomous deployment while enforcing efficiency and resource constraints.

Problem

• Informative Path Planning
• Robotic Exploration
• Resource Constraints
• Mars Exploration
• Multi-objective Optimization

Innovation

• Expected Free Energy
• Active Inference
• Informative Path Planning
• Gaussian Process
• Budgeted Exploration