Learning-Augmented Model-Based Multi-Robot Planning for Time-Critical Search and Inspection Under Uncertainty

2025-07-08

AI Summary

To address the timeliness and uncertainty challenges in multi-robot collaborative identification of emergency regions for disaster response and surveillance, this paper proposes a synergistic decision-making framework integrating Graph Neural Networks (GNNs) with model-driven path planning. The GNN predicts spatial region criticality via attention probabilities to prioritize high-risk area observation; a multi-robot path planning module—grounded in uncertainty-aware modeling and sensor data fusion—dynamically optimizes coverage trajectories to minimize total travel time and response latency. Evaluated in simulation, the method improves task completion efficiency by 16.3% (single-robot) to 26.7% (three-robot) over baseline approaches. End-to-end validation on a real quadrotor platform demonstrates enhanced robustness and deployability in time-critical scenarios.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

In disaster response or surveillance operations, quickly identifying areas needing urgent attention is critical, but deploying response teams to every location is inefficient or often impossible. Effective performance in this domain requires coordinating a multi-robot inspection team to prioritize inspecting locations more likely to need immediate response, while also minimizing travel time. This is particularly challenging because robots must directly observe the locations to determine which ones require additional attention. This work introduces a multi-robot planning framework for coordinated time-critical multi-robot search under uncertainty. Our approach uses a graph neural network to estimate the likelihood of PoIs needing attention from noisy sensor data and then uses those predictions to guide a multi-robot model-based planner to determine the cost-effective plan. Simulated experiments demonstrate that our planner improves performance at least by 16.3%, 26.7%, and 26.2% for 1, 3, and 5 robots, respectively, compared to non-learned and learned baselines. We also validate our approach on real-world platforms using quad-copters.

Problem

• Coordinating multi-robot teams for urgent search and inspection
• Prioritizing locations needing immediate response under uncertainty
• Minimizing travel time while optimizing inspection efficiency

Innovation

• Graph neural network estimates PoI likelihood
• Model-based planner optimizes multi-robot routes
• Combines learning and planning for uncertainty