Multi-Agent Collaboration and Coordination Mechanisms in Emergency Evacuation

Multi-Agent Collaboration and Coordination Mechanisms in Emergency Evacuation

Problem Description
In emergency evacuation scenarios, multiple intelligent agents (such as robots, drones, or virtual agents with decision-making capabilities) may need to collaborate to accomplish tasks like guidance, rescue, or resource allocation. Such problems require addressing the following core challenges:

  1. Goal Conflict: Agents' tasks may have different priorities (e.g., rapid evacuation vs. rescuing the injured).
  2. Resource Competition: Shared spaces or tools may lead to conflicts (e.g., exit congestion).
  3. Information Asymmetry: Agents have limited perception ranges and need to achieve coordination through communication.
  4. Dynamic Environment: Factors like fire spread and structural collapse require real-time strategy adjustments.

Problem-Solving Process

1. Problem Formalization

  • Agent Definition: Each agent has attributes (position, speed, task list, communication range).
  • Environment Modeling: Represent the space using a grid map or network graph, marking danger zones, resource points, and exits.
  • Objective Function: Define collaboration goals (e.g., minimize total evacuation time, maximize number of survivors).

2. Collaboration Mechanism Design

(1) Task Allocation and Role Assignment

  • Centralized Allocation: A central controller (e.g., a server) assigns tasks based on global information (e.g., Drone A is responsible for scouting the fire, Robot B guides the crowd).
    • Advantages: Global optimal solutions are easier to achieve.
    • Disadvantages: Risk of single point of failure.
  • Distributed Negotiation: Agents autonomously negotiate tasks through voting, auctions, or contract net protocols.
    • Example: An agent broadcasts "I can handle the guidance task in area X," and other agents respond and coordinate.

(2) Conflict Resolution Strategies

  • Spatial Conflict: Use traffic rules (e.g., keep right) or reservation mechanisms (agents request path usage rights in advance).
  • Resource Conflict: Introduce priority systems (e.g., ambulance robots pass congested points first) or dynamic scheduling (allocate resources based on urgency).

3. Communication and Information Fusion

  • Communication Protocol: Design lightweight message formats (e.g., position, task status, danger alerts) to avoid channel congestion.
  • Information Fusion: After sharing local perception data, agents generate a global map using Kalman filtering or Bayesian estimation to reduce uncertainty.

4. Dynamic Adjustment and Robustness

  • Real-time Replanning: Regularly assess task progress; if an agent fails (e.g., runs out of power), other agents take over its tasks.
  • Fault Tolerance Mechanisms: Prepare alternative paths or redundant agents to cope with sudden dangers (e.g., a blocked exit).

5. Simulation and Evaluation

  • Tools: Use NetLogo, ROS, or specialized multi-agent simulation platforms to model scenarios.
  • Metrics: Compare evacuation efficiency (e.g., average escape time), task completion rate, and conflict frequency under different collaboration mechanisms.

Summary
The core of multi-agent collaboration lies in integrating individual behaviors into efficient collective action through task allocation, conflict resolution, and communication design. Practical applications require balancing the pros and cons of centralized control and distributed autonomy, and dynamically optimizing based on specific scenarios.