Expected Free Energy-based Informative Path Planning for Robotic Mars Exploration
2026-08-14 • Robotics
RoboticsInformation TheoryMachine Learning
AI summaryⓘ
The authors present a method for robots exploring unknown areas, like Mars, that helps them decide where to go and what to measure efficiently. Their approach uses something called Expected Free Energy (EFE) to balance learning about the environment and finding important spots while keeping travel and measurement costs low. By using a mathematical model to predict information, their robot plans paths that save resources and find valuable targets better than older methods. This strategy makes robot exploration smarter and more practical when resources are limited.
Expected Free EnergyActive InferenceGaussian ProcessRobotic ExplorationInformative Path PlanningInformation TheoryPosterior MappingPath-Length ConstraintsAutonomous Robots
Authors
Ajith Anil Meera, Pablo Lanillos, Wouter Kouw
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.