Reproduce “Approximate Sequential Optimization for Informative Path Planning” on OpenResearch

About this paper

Joshua Ott, Mykel J. Kochenderfer, Stephen Boyd

Researchers from Stanford University developed Approximate Sequential Path Optimization (ASPO) for Informative Path Planning (IPP), a method that efficiently determines paths to maximize information gain in large-scale environments. ASPO consistently outperforms other methods on graphs exceeding 5000 nodes for various objectives, significantly reducing planning times while robustly handling adaptive planning and multi-agent coordination without requiring retraining.

Key insights

• The B-IPP objective, minimizing the trace of the inverse of the information matrix, proves to be an effective and computationally efficient surrogate for A-IPP and D-IPP, allowing for more tractable MILP solutions.
• ASPO's combination of a receding horizon strategy and dynamic programming for local path segments enables it to scale to large graph sizes while maintaining competitive solution quality.
• The model-based nature of ASPO provides robustness, allowing direct adaptation to changes in Gaussian process hyperparameters, graph structures, budgets, or objectives without requiring extensive retraining, unlike deep reinforcement learning approaches.

Summary by alphaXiv.