Swarm Robotics for Crisis Management

Overview

Disaster response and crisis management require rapid, coordinated action across complex, dynamic environments. This project investigates swarm robotics as an approach to autonomous, decentralized crisis response — where a collective of simple robots coordinates to achieve goals no single robot could accomplish alone.

Motivation

Traditional disaster response robots are expensive and require human teleoperation. Swarm systems offer:

• Scalability — deploy many low-cost robots
• Resilience — the swarm continues functioning even if individual units fail
• Adaptability — emergent collective behavior adapts to changing environments
• Parallelism — multiple tasks (search, rescue, mapping) executed simultaneously

Methodology

• Swarm intelligence algorithms — stigmergy, flocking, foraging-inspired coordination
• AI-driven decision making — local sensing and simple rules produce globally coherent behavior
• Scenario modeling — simulated crisis environments (search and rescue, area mapping, hazard containment)
• Coordination strategies — role assignment, task allocation, and communication in degraded environments

Key Contributions

• Survey and analysis of swarm coordination strategies for crisis scenarios
• AI-enhanced coordination framework for heterogeneous swarm teams
• Performance evaluated under varying team sizes, failure rates, and environment complexity

Publication

Published in AI and Machine Learning for Mechanical and Electrical Engineering, pp. 182–197, Auerbach Publications (Taylor & Francis), 2025.

(Dandge et al., 2025)

Status

Published — Auerbach Publications, 2025.