In the realm of embedded artificial intelligence (AI), tasks that seem straightforward to humans can often prove to be complex and resource-intensive. One such challenge is enabling a robot or AI-powered device to open a door. This seemingly simple task involves several intricate steps that require the AI to:

• Detect the door within its environment.
• Identify the door's type: Is it a push/pull, sliding, or automatic door? Is it a combination of these?
• Locate the handle and determine its type (e.g., knob, lever) and its direction of use.
• Understand the door's movement: does it swing open, slide, or open in some other manner.
• Determine whether the door remains open or closes automatically.

Furthermore, each new embedded AI tasked with interacting with a door would need to go through this same process each time, resulting in a significant amount of trial and error. For each environment, the AI would need to "learn" the characteristics of every door, meaning that it would require new data for each interaction. The AI also likely wouldn't retain memory of doors it has already encountered, which leads to inefficiencies and delays.

But what if there was a more efficient solution? Enter collaborative spatial mapping with the Auki network, a game-changing approach that could streamline the process and save valuable time for embedded AI systems.

Collaborative Spatial Mapping with the Auki network: The Future of Efficient AI Interaction

With the Auki network, a door, along with all its relevant characteristics, can be added to a shared digital map. Devices such as robots, smart glasses, or even mobile phones can detect and catalog the door. Once the door is mapped, all AI devices that use the Auki network can access the shared data, making door interaction faster and more accurate.

The beauty of this system is that it is dynamic and collaborative. Multiple devices or users can contribute additional data to improve the map, including:

• Door Type:
• Handle Position and Type:
• Operational Direction:
• Access Requirements:

The Advantages of Collaborative Spatial Mapping with the Auki network

Embedded AI excels in dynamic, ever-changing environments, where real-time adaptation is crucial. However, many aspects of the physical world, such as doors and other infrastructure, are relatively static. Why should every new AI system analyze the same door repeatedly when it can be cataloged, mapped, and shared across all systems using the Auki network?

Collaborative spatial mapping via the Auki network allows AI to take advantage of static data, reducing redundant analysis and enabling faster decision-making. This shared knowledge base is particularly useful in environments where frequent interactions with common objects, like doors, are necessary.

Additionally:

• Inter-device Collaboration: Every device within a system, whether a robot, mobile phone, or smart glasses, can access and update shared data in real-time, ensuring that the information is always current and relevant.
• Real-time Data Updates: If the door changes state, locked, handle repositioned, or out of service, the Auki network can immediately update and push this new data to all devices, ensuring consistent, up-to-date information for all.

Conclusion: A Smarter Approach to AI-Driven Interactions with the Auki network

By embracing collaborative spatial mapping through the Auki network, the complexity of tasks like opening a door can be simplified for embedded AI systems.

Rather than starting from scratch each time a new device encounters a door, the device can rely on an existing map of information. This not only saves time and reduces trial and error, but also ensures that every device interacting with a door has the most up-to-date and relevant data available.

In environments where collaboration between devices is key to optimizing performance, the use of the Auki network provides a more streamlined, efficient, and intelligent way to handle tasks that might otherwise be unnecessarily complex.

About Auki Labs

Auki is building the Auki network, a decentralized machine perception network for the next 100 billion people, devices and AI on Earth and beyond. The Auki network is a posemesh, an external and collaborative sense of space that machines and AI can use to understand the physical world.

Our mission is to improve civilization’s intercognitive capacity; our ability to think, experience and solve problems together with each other and AI. The greatest way to extend human reach is to collaborate with others. We are building consciousness-expanding technology to reduce the friction of communication and bridge minds.

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About the Auki network

The Auki network is a posemesh: a decentralized machine perception network and collaborative spatial computing protocol, designed to allow digital devices to securely and privately exchange spatial data and computing power to form a shared understanding of the physical world.

The Auki network is an open-source protocol that powers a decentralized, blockchain-based spatial computing network. Designed for a future where spatial computing is both collaborative and privacy-preserving, it limits any organization's surveillance capabilities and encourages sovereign ownership of private maps of personal and public spaces.

The decentralization also offers a competitive advantage, especially in shared spatial computing sessions, AR for example, where low latency is crucial.

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