Systems, apparatuses, and methods for a distributed robotic network of data collection and insight generation
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Solution Overview
Problem
Current robotic systems collect a vast amount of data, much of which is of marginal utility for the robots' primary functions, but could be valuable for additional functionalities, human insights, or optimizing robotic operations.
Innovation Solution
A distributed robotic network and AI marketplace system that enables data collection from various sources, including robots, IoT devices, and stationary sensors, and processes this data to generate insights. This system allows for the storage of data in a marketplace, execution of applications to derive insights, and feedback mechanisms to optimize data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If robots collect extensive data from multiple sensors, then data quantity increases, but data utility for primary robot functions decreases
Solution Approach 1:
The patent introduces a server as an intermediary between multiple robots and data collection sources. The server receives data from various sensors (cameras, LiDAR, IMUs) across the robotic network, processes and stores it centrally, and makes it available for multiple uses. This intermediary structure allows raw data to be collected in bulk without each robot needing to process all data locally, thereby maintaining data quantity while improving effective utilization through centralized management and multi-purpose application.
2Adaptability or versatility
If a distributed robotic network collects data from multiple sources, then data comprehensiveness improves, but system complexity increases
Solution Approach 1:
The patent segments the distributed robotic network into independent functional modules: data collection sources (sensors on robots), a central server for data management, and data consumers (applications). Each component operates autonomously with defined interfaces. The server segments data processing into receiving, storing, and distributing functions. This segmentation allows the system to handle comprehensive data from multiple sources while managing complexity through modular architecture and clear separation of concerns.
3Productivity
If robots utilize collected data for additional functionalities, then robot efficiency improves, but data processing requirements increase
Solution Approach 1:
The patent enables robots to self-service by providing them with access to processed data and insights generated by the central server. Instead of requiring complex local processing capabilities on each robot, the system allows robots to query the server for relevant data and insights, which are already processed and organized. This self-service model improves robot efficiency through better decision-making while avoiding the need to increase local processing power on individual robots, as the heavy processing is performed centrally.
Data Source
AI summary
Systems, apparatuses, and methods for a distributed network of data collection and insight generation by server are disclosed herein. According to at least one non-limiting exemplary embodiment, the server may be configured to receive data from a network of data sources, receive an application from an application creator, and execute the application based on the data from the network of data sources to generate at least one insight, wherein the network of data sources may comprise mobile robots, stationary devices, IoT (Internet of Things) devices, and/or public data sources. The at least one insight may be utilized by robots to improve efficiency of operation or by humans to gain useful insights to the environment in which the data sources operate.