Study on Energy Aware Task Allocation and Optimization Strategies in Swarm Robotic Systems

This work focuses on implementing an energy-aware task allocation and navigation system for a swarm of e-puck robots in a simulated environment using Webots. Each robot monitors its battery level to make the required decisions like selecting tasks in one of multiple aisles, performing assigned operations, waiting at designated points, and returning to a charging station when energy falls below a critical threshold. The system optimizes energy consumption across the swarm while ensuring task completion, reducing idle time, and preventing collisions. This approach demonstrates how autonomous robots can efficiently manage resources in a collaborative multi-robot system, providing insights into real-world applications of energy-aware swarm intelligence in logistics, warehouse management, and industrial automation