⚠️ This material is just a placeholder … it’s still only a rough draft of highly theoretical psuedo-science. ⚠️

Precepts

The CloudKernelOS will be architecturally based upon the theoretical principles of Self-organizing Nervous Systems(SoNS) … we will need to develop those principles; at this time, the following material is only preliminary scribbling that serves as placeholder before we have a core outline that will be developed into a very rough draft.

CloudKernelOS is about pheromonic or neuromorphic computing with evolving morphic or noisy steganographic communication akin to electrochemically explorational 🍄 fungal mycellium or 🍄 hyphae found in 🍄 mycorrhizal networks 🍄 for the semiotic signalling that might enabling complex swarm behaviors in real-time operating systems of loosely-networked distributed autonomous robotics. 🐝

🐝Thus, we envision a RT/FT operation system whose I/O is designed to priortize and filter the noisy pheromonic signals of a swarm network. The processing of signals would rely on data affirmation and confidence assessment or weighted judgement of noisy signals coming from the electrochemically explorational hyphae found in 🍄 mycorrhizal networks or swarms of coordinated noisy buzzing autonomous robotic units 🐝 to overcome the wind of other radio signals 📶 or weakness of any one single signal 📶 in a multiplexed communication fabric of highly faulty, tangled message threads, but the flip side of this is about the simplicity of durable intelligently-networked autonomous units. In some sense, it’s is really about push for extreme simplification of real-time computing moving toward fault-tolerant computing, not just beyond edge computing into dew computing, off the edge and into the fog and toward fog computing, toward putting all of the computing power at the edge, for mist and multi-access computing and radio-access networks for improving RELIABILITY, reducing fragility and building strength of the system through constantly-improving redundancy. 🏋️ 🐝

SoNS Architecture

The general principles of Self-organizing Nervous Systems(SoNS) is our starting point for thinking about the architecture of robot swarms. The concept of SoNS addresses what we see as the critical dichotomy in thinking about an architecture for swarm robotics systems: the trade-off between centralized control and decentralized autonomy.

Traditional Approaches:

Centralized Systems: These systems have a single point of control, which can be limiting due to potential points of failure and scalability challenges.

Decentralized Systems: These are fully self-organized but can be challenging to design analytically.

The SoNS Approach:

The Self-organizing Nervous System (SoNS) is a novel architecture that bridges this gap between centralized and decentralized systems.

It enables robots to autonomously establish, maintain, and reconfigure dynamic multi-level system architectures.

Imagine a robot swarm consisting of n independent robots. With SoNS:

  • It can transform into a single n-robot SoNS.
  • Then, it can further divide into several independent smaller SoNSs.
  • Each SoNS uses a temporary and dynamic hierarchy.
  • SoNS allows for locally centralized coordination without sacrificing scalability, flexibility, or fault tolerance.
  • Sensing, actuation, and decision-making become more efficient within this framework.

Applications and Advancements of SoNS

SoNS has been demonstrated in various robot missions, including binary decision-making and search-and-rescue scenarios.

Real heterogeneous aerial-ground robot swarms have been used for these missions.

The capabilities of SoNS significantly advance the state of the art in swarm robotics.

Scalability and Fault Tolerance

SoNS has been tested with swarms of up to 250 robots in a physics-based simulator.

It exhibits robustness and fault tolerance in both simulation and real-world scenarios.

Summary

The Self-organizing Nervous System empowers robot swarms to dynamically adapt their architecture, combining the best of both centralized and decentralized approaches.