Informative Path Planning to Explore and Map Unknown Planetary Surfaces with Gaussian Processes
This addresses the challenge of efficient autonomous exploration for unvisited environments like planetary surfaces, though it is incremental as it builds on existing Gaussian process methods.
The study tackled the problem of autonomously mapping unknown planetary surfaces by comparing traditional coverage methods with information-theoretic approaches using Gaussian processes, resulting in significant outperformance in reducing model error and travel distance while improving convergence potential.
Many environments, such as unvisited planetary surfaces and oceanic regions, remain unexplored due to a lack of prior knowledge. Autonomous vehicles must sample upon arrival, process data, and either transmit findings to a teleoperator or decide where to explore next. Teleoperation is suboptimal, as human intuition lacks mathematical guarantees for optimality. This study evaluates an informative path planning algorithm for mapping a scalar variable distribution while minimizing travel distance and ensuring model convergence. We compare traditional open loop coverage methods (e.g., Boustrophedon, Spiral) with information-theoretic approaches using Gaussian processes, which update models iteratively with confidence metrics. The algorithm's performance is tested on three surfaces, a parabola, Townsend function, and lunar crater hydration map, to assess noise, convexity, and function behavior. Results demonstrate that information-driven methods significantly outperform naive exploration in reducing model error and travel distance while improving convergence potential.