Design for One, Deploy for Many: Navigating Tree Mazes with Multiple Agents

2025-10-30

AI Summary

This work addresses the challenges of multi-robot collaborative navigation in tree-structured mazes (e.g., caves, pipelines), including communication constraints, local congestion, and poor scalability. We propose a distributed collaborative navigation algorithm centered on a dynamic leader-switching mechanism: each robot autonomously elects and transfers leadership based solely on local sensing, eliminating the need for global communication while emulating centralized single-agent path planning behavior—thereby ensuring system consistency and scalability. The algorithm integrates classical single-agent maze-solving strategies tailored to tree-graph topologies. Extensive simulations with up to 300 agents across multi-scale mazes demonstrate superior efficiency: significantly reduced task completion time and energy consumption compared to baseline methods. Physical validation using 20 Pi-puck robots confirms the algorithm’s feasibility, robustness, and real-world applicability.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Distributed Problem Solving
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Maze-like environments, such as cave and pipe networks, pose unique challenges for multiple robots to coordinate, including communication constraints and congestion. To address these challenges, we propose a distributed multi-agent maze traversal algorithm for environments that can be represented by acyclic graphs. It uses a leader-switching mechanism where one agent, assuming a head role, employs any single-agent maze solver while the other agents each choose an agent to follow. The head role gets transferred to neighboring agents where necessary, ensuring it follows the same path as a single agent would. The multi-agent maze traversal algorithm is evaluated in simulations with groups of up to 300 agents, various maze sizes, and multiple single-agent maze solvers. It is compared against strategies that are naïve, or assume either global communication or full knowledge of the environment. The algorithm outperforms the naïve strategy in terms of makespan and sum-of-fuel. It is superior to the global-communication strategy in terms of makespan but is inferior to it in terms of sum-of-fuel. The findings suggest it is asymptotically equivalent to the full-knowledge strategy with respect to either metric. Moreover, real-world experiments with up to 20 Pi-puck robots confirm the feasibility of the approach.

Problem

• Develops distributed multi-agent maze navigation for acyclic graphs
• Addresses communication constraints and congestion in robot coordination
• Proposes leader-switching mechanism using single-agent solvers as base

Innovation

• Distributed multi-agent maze traversal algorithm
• Leader-switching mechanism for role transfer
• Acyclic graph representation for maze environments