PRoID: Predicted Rate of Information Delivery in Multi-Robot Exploration and Relaying

2026-04-11

AI Summary

This work addresses the challenge of coordinating multi-robot exploration under time constraints in unknown environments, where robots must dynamically balance exploration with timely information relay—a task poorly handled by conventional fixed-schedule strategies. The authors propose PRoID, a novel relay decision criterion that triggers data transmission based on maximizing the predicted rate of information delivery per unit time, integrating map predictions, path-based information gain, and teammates’ already-transmitted data. They further introduce PRoID-Safe, which incorporates robot survival probability to enable risk-aware, adaptive relaying. Experimental results on real-world indoor datasets demonstrate that both methods significantly outperform baseline approaches, with particularly pronounced improvements in high-failure-risk 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: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We address Multi-Robot Exploration and Relaying (MRER): a team of robots must explore an unknown environment and deliver acquired information to a fixed base station within a mission time limit. The central challenge is deciding when each robot should stop exploring and relay: this depends on what the robot is likely to find ahead, what information it uniquely holds, and whether immediate or future delivery is more valuable. Prior approaches either ignore the reporting requirement entirely or rely on fixed-schedule relay strategies that cannot adapt to environment structure, team composition, or mission progress. We introduce PRoID (Predicted Rate of Information Delivery), a relay criterion that uses learned map prediction to estimate each robot's future information gain along its planned path, accounting for what teammates are already relaying. PRoID triggers relay when immediate return yields higher information delivery per unit time. We further propose PRoID-Safe, a failure-aware extension that incorporates robot survival probability into the relay criterion, naturally biasing decisions toward earlier relay as failure risk grows. We evaluate on real-world indoor floor plan datasets and show that PRoID and PRoID-Safe outperform fixed-schedule baselines, with stronger relative gains in failure scenarios.

Problem

• Multi-Robot Exploration and Relaying
• Information Delivery
• Relay Decision
• Mission Time Limit
• Base Station Communication

Innovation

• multi-robot exploration
• information delivery
• relay strategy
• map prediction
• failure-aware planning