Maps provide spatial awareness for AI agents and robots, enabling them to navigate and interact effectively in physical environments, which is essential for applications that depend on real-world positioning and coordination. Whereas traditional maps are often centralized and proprietary, decentralized maps have the potential to unlock new innovation, reduce costs and improve interoperability. Decentralized mapping will be key to inviting robots and AI into our real world spaces.
Decentralized maps are poised to play a crucial role in enabling the machine-to-machine (M2M) economy by providing real-time, secure, and trustless spatial data that machines and AI agents can use to interact autonomously. They facilitate autonomous vehicles, drones, and IoT devices to access up-to-date location data, enabling seamless navigation, collaboration, and decision-making.
Collaborative mapping systems can utilize contributions from participating devices and machines to gather and share location-based data, which is then stored and updated across a distributed network, potentially freeing up machines from relying on their own sensor data and computationally heavy processes like object recognition and classification. This could allow future robots and machines to be smaller, lighter and more nimble. These maps could also improve interoperability between machines from different manufacturers, improving efficiency and scalability.
Interoperable, collaborative mapping systems will unlock the full potential of robots, devices and AI, inviting them into the real world and transforming them into a unified, intelligent system.
Autonomous Awareness
Autonomous vehicles, drones, robots and AI rely on spatial maps to navigate, avoid obstacles, reach destinations and comprehend their environments.
Real-Time Efficiency
Spatial maps help machines identify nearby resources such as charging stations or storage facilities in real time.
Contextual Transactions
Machines need spatial context to make intelligent decisions during transactions. Spatial maps provide this by linking physical locations to digital identities and attributes, such as a self-driving car identifying available parking spots close to its destination, and then negotiating a microtransaction to reserve the space.
Machine Collaboration
Many M2M applications involve multiple devices working together. Spatial maps allow machines to coordinate actions by having a shared understanding of space. In logistics, autonomous vehicles and drones can collaborate using spatial maps to ensure efficient package delivery, avoiding collisions and optimizing routes.
Decentralized Scalability
Spatial maps can distribute knowledge across the network, eliminating the need for centralized coordination, enhancing scalability and fault tolerance. Such as a decentralized energy grid, where spatial maps enable machines to identify nearby energy producers and consumers, balancing supply and demand dynamically.
Systemic Interoperability
Devices from different manufacturers or sectors need a shared spatial framework to interact seamlessly. Spatial maps provide this universal reference. A delivery robot from one company could use a collaborative spatial map to navigate through a warehouse managed by another company, ensuring smooth operations.
Usecases
Autonomous Vehicles
Spatial maps enable vehicles to navigate complex urban environments, find parking, and interact with traffic systems.
Logistics
An autonomous forklift and delivery drone from different manufacturers can collaborate using spatial maps to ensure efficient package delivery, avoiding collisions and optimizing routes.
Retail
An autonomous forklift and delivery drone from different manufacturers can collaborate using spatial maps to ensure efficient package delivery, avoiding collisions and optimizing routes.
Partners
Learn how our partners are addressing the pillars of the Intercognitive Foundation.
