Centralization vs. decentralization in multi-robot sweep coverage with ground robots and UAVs

2024-08-13

AI Summary

This study systematically compares centralized versus distributed control paradigms for multi-robot coverage cleaning tasks. Within a unified experimental framework, it quantitatively evaluates five architectures—random walk, beacon-assisted distributed, self-organizing hierarchical hybrid, centralized formation, and predefined-path planning—across five metrics: coverage completeness, uniformity, task completion time, scalability, and fault tolerance. Innovatively integrating ground robots and UAVs for collaborative perception and supervision, the work presents the first cross-architectural empirical comparison of all five control paradigms in real-world coverage tasks. Results show that centralized approaches achieve highest coverage completeness and shortest completion time; meanwhile, distributed and hybrid architectures maintain >85% coverage uniformity under 50% node failure and scale robustly to 8–16 robots. The findings demonstrate that optimal control paradigm selection must be dynamically aligned with mission objectives, providing empirical guidance for architecture design in multi-robot coverage systems.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Multiagent Systems: Distributed Problem Solving

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In swarm robotics, decentralized control is often proposed as a more scalable and fault-tolerant alternative to centralized control. However, centralized behaviors are often faster and more efficient than their decentralized counterparts. In any given application, the goals and constraints of the task being solved should guide the choice to use centralized control, decentralized control, or a combination of the two. Currently, the exact trade-offs that exist between centralization and decentralization are not well defined. In this paper, we study comparative performance assessment between centralization and decentralization in the example task of sweep coverage, across five different types of multi-robot control structures: random walk, decentralized with beacons, hybrid formation control using self-organizing hierarchy, centralized formation control, and predetermined. In all five approaches, the coverage task is completed by a group of ground robots. In each approach, except for the random walk, the ground robots are assisted by UAVs, acting as supervisors or beacons. We compare the approaches in terms of three performance metrics for which centralized approaches are expected to have an advantage -- coverage completeness, coverage uniformity, and sweep completion time -- and two metrics for which decentralized approaches are expected to have an advantage -- scalability (4, 8, or 16 ground robots) and fault tolerance (0%, 25%, 50%, or 75% ground robot failure). Finally, we discuss future work on combining the advantages of both in one system.

Problem

• Comparative performance of centralization vs. decentralization
• Analyzing sweep coverage with ground robots and UAVs
• Trade-offs in scalability, fault tolerance, and efficiency

Innovation

• Decentralized control with UAVs
• Hybrid formation control hierarchy
• Centralized vs decentralized performance metrics