Cloud Robotics Intelligence With Human Review for Adaptive Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robotic systems require significant computational power and storage to process and learn from environmental data locally, making it burdensome and difficult for robots to share knowledge and adapt to new environments, and they lack cognitive capabilities to recognize changing parameters and respond accordingly.
Innovation Solution
A human augmented robotics intelligence system that utilizes cloud computing and data storage, where robots transmit sensor data to a cloud-based platform for processing, leveraging historical databases of similar robots to determine appropriate actions, and employs AI and deep learning to recognize patterns and generate executable commands for tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If robots utilize on-board processing and storage to retain movement protocols and make determinations, then robots can operate autonomously with real-time decision making, but robots require significant computational power and storage capacity which increases device complexity and cost
Solution Approach 1:
The patent introduces a cloud-based platform as an intermediary between robots and users. The cloud platform performs complex processing, storage, and protocol development, while robots execute simpler local commands. This mediator approach allows robots to operate autonomously without requiring significant onboard computational resources.
Solution Approach 2:
The patent extracts the heavy computational and storage requirements from the robot system and relocates them to a cloud-based platform. The cloud platform retains movement protocols and environmental data, while robots receive processed commands, thereby reducing onboard device complexity.
2Speed
If robots process and learn from environmental data locally, then robots can respond quickly to changing conditions, but it becomes burdensome and difficult for robots to share knowledge and adapt to new environments
Solution Approach 1:
The patent merges multiple robots into a unified cloud-based system where environmental data and learning outcomes are combined into shared protocols. The cloud platform aggregates data from multiple robots, processes it centrally, and distributes updated protocols to all robots, enabling knowledge sharing while maintaining individual robot autonomy.
Solution Approach 2:
The patent implements a feedback loop where robots transmit environmental data to the cloud platform, which processes the data and generates updated movement protocols. These protocols are fed back to robots for execution, creating a continuous learning and adaptation cycle that improves collective robot performance over time.
3Device complexity
If robots lack cognitive capabilities to recognize changing parameters, then robot systems are simpler with lower computational requirements, but robots cannot adapt to new environments or respond appropriately to varying conditions
Solution Approach 1:
The cloud-based platform serves as a cognitive intermediary that recognizes changing environmental parameters and translates them into actionable protocols. Robots maintain simplicity by executing cloud-generated commands rather than performing complex environmental analysis themselves, thereby achieving adaptability without increasing onboard complexity.
Data Source
AI summary
A human augmented robotics intelligence operation system can include a plurality of robots, each robot having a plurality of sensors; a robot control unit; and one or more articulating joints; a cloud-based robotic intelligence engine having; a communication module; a historical database; and a processor; and a human augmentation platform. The processor can be configured to make a probabilistic determination regarding the likelihood of successfully completing the particular user command. When the probabilistic determination is above a pre-determined threshold, the processor sends necessary executable commands to the robot control unit. Alternatively, when the probabilistic determination is below the predetermined threshold, the processor generates an alert and flags the operation for human review.