Swarm-based coordination architecture for humanoid robots: A distributed multi-agent framework with secure rule evolution

Abstract

Humanoid robots require scalable, adaptive, and fault-tolerant coordination mechanisms to manage the high dimensionality and interdependence of their joint systems. Building on our previous work on distributed multi-agent control with secure blackboard-based coordination, this paper introduces a swarm-based architecture that models the humanoid robot as a collection of interacting joint-agents governed by emergent swarm rules. Each joint operates as an autonomous agent with local perception and actuation, while global motion is achieved through decentralized behaviors such as alignment, cohesion, and stability-seeking interactions. We propose a hierarchical swarm-control framework that separates rule execution at the joint level from rule evolution at the coordination layer. A secure rule evolution mechanism, inspired by lightweight blockchain validation, ensures that modifications to swarm parameters such as alignment weights or neighborhood influence scopes are consistent, safe, and cryptographically verifiable. This enables online adaptation while preventing unsafe or malicious updates to the robot’s coordination strategy. The architecture is validated through distributed 2D swarm simulations demonstrating that stable whole-body behavior can emerge from local swarm rules without requiring centralized trajectory optimization. Results show improved robustness against joint disturbances, faster adaptation to configuration changes, and natural scalability as the number of joints increases. The proposed framework establishes a foundation for future humanoid control systems where autonomy arises from the cooperative dynamics of swarm intelligence under secure, auditable coordination protocols. The implementation code is publicly available to ensure experimental reproducibility.