Cloud-Based Shared Robot Knowledge Base for Object Recognition
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Solution Overview
Problem
Current cloud computing systems lack an efficient method for robots to identify and interact with objects in their environment, as they rely on onboard knowledge bases that are limited in scope and require constant updates, leading to inaccuracies and inefficiencies in object recognition and task execution.
Innovation Solution
A shared robot knowledge base within a cloud computing system that allows robots to query and update data on objects, tasks, and maps, enabling collective learning and improved interaction capabilities through a networked architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robots use onboard knowledge bases for object identification, then they can operate independently, but the knowledge base scope is limited and requires constant updates leading to inaccuracies
Solution Approach 1:
The patent merges individual robot knowledge bases into a centralized cloud-based shared knowledge base. Multiple robots contribute object data, images, and identification results to a common repository accessible by all robots in the network, allowing collective learning and improved accuracy without limiting independent operation.
Solution Approach 2:
The system implements feedback mechanisms where robots upload identification results, corrections, and new object encounters to the cloud knowledge base. The knowledge base is continuously updated based on feedback from multiple robots, improving identification accuracy over time while maintaining robot independence.
2Ease of operation
If robots maintain individual knowledge bases, then they operate independently, but updating and synchronizing data across all robots is time-consuming and inefficient
Solution Approach 1:
The patent introduces a cloud-based shared knowledge base as an intermediary between individual robots. Instead of direct robot-to-robot communication for data sharing, the cloud server acts as a central mediator that stores, manages, and distributes knowledge data, eliminating the need for time-consuming peer-to-peer synchronization.
Solution Approach 2:
The system transitions from a distributed horizontal knowledge sharing model (robot-to-robot) to a centralized vertical model (cloud-based). By adding the cloud dimension, the system enables asynchronous data uploads and downloads, allowing robots to operate independently while the cloud handles synchronization in the background.
3Device complexity
If robots use limited onboard knowledge bases, then device complexity is reduced, but the scope of objects and tasks they can identify is restricted
Solution Approach 1:
The patent extracts the large-scale knowledge base from individual robot devices and relocates it to a cloud environment. Robots retain only minimal local caching for immediate operations, while the comprehensive knowledge base including object databases, images, and task information is stored and managed on the cloud server, expanding capabilities without increasing device complexity.
Data Source
AI summary
The present application discloses shared robot knowledge bases for use with cloud computing systems. In one embodiment, the cloud computing system collects data from a robot about an object the robot has encountered in its environment, and stores the received data in the shared robot knowledge base. In another embodiment, the cloud computing system sends instructions for interacting with an object to a robot, receives feedback from the robot based on its interaction with the object, and updates data in the shared robot knowledge base based on the feedback. In yet another embodiment, the cloud computing system sends instructions to a robot for executing an application based on information stored in the shared robot knowledge base. In the disclosed embodiments, information in the shared robot knowledge bases is updated based on robot experiences so that any particular robot may benefit from prior experiences of other robots.