REACT: Multi Robot Energy-Aware Orchestrator for Indoor Search and Rescue Critical Tasks

2025-03-07

AI Summary

This work addresses indoor search-and-rescue (SAR) missions in intelligent factories under catastrophic events (e.g., fire, earthquake), where environments feature high-density unknown obstacles and stringent energy constraints. Method: We propose a multi-robot collaborative energy-aware scheduling framework. Its core innovation lies in embedding dynamic energy optimization directly into the closed-loop collaborative exploration decision process—addressing the critical limitation of conventional approaches that neglect explicit energy modeling. The method integrates real-time multi-source sensing, distributed consensus-based communication, and online collaborative path re-planning, augmented by dynamic load balancing and redundant-motion suppression mechanisms to jointly ensure timeliness, coverage completeness, and endurance robustness. Results: Simulation results demonstrate a 10% improvement in exploration rate, 97% map coverage within emergency time limits, and 99.8% coverage under relaxed deadlines—significantly enhancing SAR efficiency and system sustainability.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Smart factories enhance production efficiency and sustainability, but emergencies like human errors, machinery failures and natural disasters pose significant risks. In critical situations, such as fires or earthquakes, collaborative robots can assist first-responders by entering damaged buildings and locating missing persons, mitigating potential losses. Unlike previous solutions that overlook the critical aspect of energy management, in this paper we propose REACT, a smart energy-aware orchestrator that optimizes the exploration phase, ensuring prolonged operational time and effective area coverage. Our solution leverages a fleet of collaborative robots equipped with advanced sensors and communication capabilities to explore and navigate unknown indoor environments, such as smart factories affected by fires or earthquakes, with high density of obstacles. By leveraging real-time data exchange and cooperative algorithms, the robots dynamically adjust their paths, minimize redundant movements and reduce energy consumption. Extensive simulations confirm that our approach significantly improves the efficiency and reliability of search and rescue missions in complex indoor environments, improving the exploration rate by 10% over existing methods and reaching a map coverage of 97% under time critical operations, up to nearly 100% under relaxed time constraint.

Problem

• Optimizes energy management for search and rescue robots.
• Enhances exploration efficiency in complex indoor environments.
• Improves operational time and area coverage in emergencies.

Innovation

• Energy-aware orchestrator optimizes exploration phase
• Collaborative robots with advanced sensors navigate environments
• Real-time data exchange reduces energy consumption