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Enhancing Cellular-enabled Collaborative Robots Planning through GNSS data for SAR Scenarios

Arnau Romero, Carmen Delgado, Jana Baguer, Raúl Suárez, Xavier Costa-Pérez

TL;DR

This framework determines the minimum number of robots required and their optimal paths to ensure effective coverage and timely data backhaul over mobile networks and quantifies the impact of terrain elevation data on mission time and energy consumption.

Abstract

Cellular-enabled collaborative robots are becoming paramount in Search-and-Rescue (SAR) and emergency response. Crucially dependent on resilient mobile network connectivity, they serve as invaluable assets for tasks like rapid victim localization and the exploration of hazardous, otherwise unreachable areas. However, their reliance on battery power and the need for persistent, low-latency communication limit operational time and mobility. To address this, and considering the evolving capabilities of 5G/6G networks, we propose a novel SAR framework that includes Mission Planning and Mission Execution phases and that optimizes robot deployment. By considering parameters such as the exploration area size, terrain elevation, robot fleet size, communication-influenced energy profiles, desired exploration rate, and target response time, our framework determines the minimum number of robots required and their optimal paths to ensure effective coverage and timely data backhaul over mobile networks. Our results demonstrate the trade-offs between number of robots, explored area, and response time for wheeled and quadruped robots. Further, we quantify the impact of terrain elevation data on mission time and energy consumption, showing the benefits of incorporating real-world environmental factors that might also affect mobile signal propagation and connectivity into SAR planning. This framework provides critical insights for leveraging next-generation mobile networks to enhance autonomous SAR operations.

Enhancing Cellular-enabled Collaborative Robots Planning through GNSS data for SAR Scenarios

TL;DR

This framework determines the minimum number of robots required and their optimal paths to ensure effective coverage and timely data backhaul over mobile networks and quantifies the impact of terrain elevation data on mission time and energy consumption.

Abstract

Cellular-enabled collaborative robots are becoming paramount in Search-and-Rescue (SAR) and emergency response. Crucially dependent on resilient mobile network connectivity, they serve as invaluable assets for tasks like rapid victim localization and the exploration of hazardous, otherwise unreachable areas. However, their reliance on battery power and the need for persistent, low-latency communication limit operational time and mobility. To address this, and considering the evolving capabilities of 5G/6G networks, we propose a novel SAR framework that includes Mission Planning and Mission Execution phases and that optimizes robot deployment. By considering parameters such as the exploration area size, terrain elevation, robot fleet size, communication-influenced energy profiles, desired exploration rate, and target response time, our framework determines the minimum number of robots required and their optimal paths to ensure effective coverage and timely data backhaul over mobile networks. Our results demonstrate the trade-offs between number of robots, explored area, and response time for wheeled and quadruped robots. Further, we quantify the impact of terrain elevation data on mission time and energy consumption, showing the benefits of incorporating real-world environmental factors that might also affect mobile signal propagation and connectivity into SAR planning. This framework provides critical insights for leveraging next-generation mobile networks to enhance autonomous SAR operations.
Paper Structure (14 sections, 11 equations, 11 figures, 4 tables, 1 algorithm)

This paper contains 14 sections, 11 equations, 11 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Cellular-enabled Collaborative Robotics Search-And-Rescue Framework
  4. Mission Planning
  5. Mission Execution
  6. Mission Planner - Under the Hood
  7. Multi-robot Resource Planner (RP) Problem
  8. Enhancing Mission Planning through GNSS data
  9. Wheeled vs Quadruped Robots - Energy Profiling
  10. Performance Evaluation
  11. Evaluation Scenario Setup
  12. Mission Planning Evaluation
  13. Uneven Terrain Evaluation
  14. Conclusions and Future Works

Figures (11)

  • Figure 1: Cellular-enabled Collaborative Robotics Search-and-Rescue Framework.
  • Figure 2: Enhancing mission planning through GNSS data pipeline.
  • Figure 3: Robots evaluated in the SAR Framework.
  • Figure 4: GO1 robot power consumption over different slopes scenarios until battery discharge
  • Figure 5: Visualization of the two considered uneven scenarios. Subfigures (a) and (b) illustrate the geographical location and the corresponding derived 3D terrain models for each scenario.
  • ...and 6 more figures