Attention-based Learning for 3D Informative Path Planning

2025-06-10

AI Summary

This work addresses the three-dimensional adaptive information path planning (IPP) problem, where an aerial robot equipped with a downward-looking sensor dynamically optimizes its 3D trajectory under spatiotemporal budget constraints to efficiently construct a high-confidence belief map of a target field (e.g., vegetation or hazardous gas concentration). We propose a novel attention-based deep reinforcement learning framework—the first to incorporate attention mechanisms into 3D IPP—to jointly model global spatial dependencies and implicitly estimate environmental dynamics, thereby unifying short-term information gain with long-term search objectives. Leveraging context-aware belief representation and sequential decision-making, our method significantly reduces environmental uncertainty within limited budgets, outperforming state-of-the-art planners. Moreover, it exhibits strong cross-scale generalization, enabling direct transfer to real-world scenarios of varying scales and complexities.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In this work, we propose an attention-based deep reinforcement learning approach to address the adaptive informative path planning (IPP) problem in 3D space, where an aerial robot equipped with a downward-facing sensor must dynamically adjust its 3D position to balance sensing footprint and accuracy, and finally obtain a high-quality belief of an underlying field of interest over a given domain (e.g., presence of specific plants, hazardous gas, geological structures, etc.). In adaptive IPP tasks, the agent is tasked with maximizing information collected under time/distance constraints, continuously adapting its path based on newly acquired sensor data. To this end, we leverage attention mechanisms for their strong ability to capture global spatial dependencies across large action spaces, allowing the agent to learn an implicit estimation of environmental transitions. Our model builds a contextual belief representation over the entire domain, guiding sequential movement decisions that optimize both short- and long-term search objectives. Comparative evaluations against state-of-the-art planners demonstrate that our approach significantly reduces environmental uncertainty within constrained budgets, thus allowing the agent to effectively balance exploration and exploitation. We further show our model generalizes well to environments of varying sizes, highlighting its potential for many real-world applications.

Problem

• Balancing sensing footprint and accuracy in 3D path planning
• Maximizing information collection under time/distance constraints
• Reducing environmental uncertainty with attention-based learning

Innovation

• Attention-based deep reinforcement learning for IPP
• Captures global spatial dependencies via attention
• Balances exploration and exploitation effectively