Offline Autonomous Robot Modules With Cloud Preprocessing
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Solution Overview
Problem
Intelligent robots require large and costly processors to operate autonomously offline, as they need to process and store extensive data and run heavy algorithms, making it impractical and costly to fit an entire server processing unit on each robot, and existing solutions rely on continuous communication with external servers which can be unreliable.
Innovation Solution
A method for an intelligent, multi-function robot to operate autonomously offline by pre-initializing and downloading necessary functionality and data from a server when online, using internal AI and limiting onboard processing power to only what is needed for preconfigured tasks, with functional modules operating in isolated virtual containers, allowing for autonomous movement and operation without external AI.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot operates autonomously offline with full functionality, then reliability and independence are improved, but processing power requirements and cost increase significantly
Solution Approach 1:
The robot system is segmented into two parts: a lightweight offline execution environment on the robot itself, and a cloud-based server that handles heavy processing tasks. The robot downloads pre-processed models and data during online phases, then executes them autonomously offline without requiring full server-grade processing power on the device.
Solution Approach 2:
Heavy processing tasks such as model training, data processing, and algorithm optimization are performed in advance on the cloud server before the robot needs to operate autonomously. The processed results are downloaded to the robot, allowing it to execute pre-computed functions offline with minimal local processing requirements.
2Adaptability or versatility
If the robot downloads and stores extensive data locally, then offline operational capability is improved, but memory requirements and device size increase
Solution Approach 1:
The system extracts only the essential, pre-processed data and model components needed for offline operation, leaving the bulk of data processing and storage requirements on the cloud server. The robot downloads compact, optimized representations of data rather than storing complete datasets locally.
3Ease of manufacture
If the robot uses a server for data processing, then processing power requirements are reduced, but dependency on external connection increases
Solution Approach 1:
The system performs preliminary processing of heavy computational tasks on the cloud server, downloading pre-computed results to the robot. This allows the robot to operate independently offline using the downloaded models and data, while still benefiting from the server's processing power for tasks that require it.
4Adaptability or versatility
If the robot fits an entire server processing unit, then full functionality offline is achieved, but device size and cost become impractical
Solution Approach 1:
The computational workload is segmented between the cloud server and the robot's local processor. The server handles intensive tasks like model training and large-scale data processing, while the robot's smaller processor executes pre-processed models and handles real-time control tasks, eliminating the need for server-grade hardware on the device.
Solution Approach 2:
Instead of placing the entire server processing unit on the robot, the system downloads copies of pre-processed models, algorithms, and essential data to the robot's local storage. These copied representations enable autonomous operation without requiring the full processing power of the original server system.
Data Source
AI summary
An intelligent, multi-function robot capable of autonomous movement includes multiple functional modules to perform functional features of the robot and a brain to activate the functional features. Each functional module is packaged in its own isolated container and each container is a virtual environment container within which the module operates. The functional features are low resource processing tasks useful for autonomous operation of the robot.