Distributed Intelligence Frameworks for Cooperative Mobile Robotics

Abstract

The deployment of multi-robot systems in dynamic, unstructured environments requires robust computational paradigms that move beyond traditional centralized processing. Distributed intelligence frameworks enable cooperative mobile robots to make autonomous decisions, share perceptual data, and synchronize tasks without relying on a single point of failure. This research proposes a hybrid consensus-driven framework that integrates decentralized edge computing, adaptive task allocation, and dynamic neighbor discovery to optimize fleet coordination. We evaluate the proposed architecture across fleet sizes ranging from 5 to 50 autonomous mobile robots (AMRs) in simulated industrial warehouse environments. Performance metrics include communication overhead, task completion efficiency, fault tolerance, and computational latency. Experimental results demonstrate that the proposed framework achieves a significant reduction in average task execution time and a substantial decrease in communication bandwidth consumption compared to standard centralized and fully peer-to-peer baseline models. Furthermore, the consensus mechanism maintains operational stability even during simulated network partitioning affecting up to 30% of active nodes. These findings underscore the feasibility of scaled distributed architectures for real-time cooperative robotics in smart manufacturing and logistics.