Decentralized Coordination in Robotic Swarms: An Adaptive Strategy for Shared Resource Management

The coordination of robotic swarms in dynamic environments requires efficient strategies for managing shared resources without centralized control. This paper presents a decentralized strategy that enables a swarm of autonomous agents to allocate and utilize resources optimally while maintaining scalability. The proposed methodology is based on distributed decision-making, where each agent interacts locally with its neighbors to regulate access to common resources while ensuring global swarm efficiency. Each agent follows a conservative redistribution logic, ensuring that the total amount of available resources remains constant throughout the swarm. The allocation process is governed by a set of local interaction rules that allow agents to negotiate resource exchanges while respecting individual constraints and global conservation principles. A key feature of the approach is the ability to adapt dynamically to variations in resource demand and availability without requiring global communication or a central coordinator. Theoretical analysis guarantees convergence properties and stability of the resource allocation process, ensuring that the system reaches a balanced distribution over time. Numerical results illustrate the effectiveness of the method, demonstrating its ability to maintain equitable resource distribution and respond to changing conditions within the swarm. The proposed strategy enhances the adaptability of robotic swarms, providing a scalable framework for decentralized resource management in multi-Agent systems.