Tiered Robot Compute Offloading for Low-Latency Autonomy
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Solution Overview
Problem
Industrial robotics faces challenges in efficiently performing autonomous operations due to high computational complexity and power consumption, which can be exacerbated by network latency and security concerns when offloading computations to servers.
Innovation Solution
A tiered network architecture is implemented, allowing for the distribution of compute resources between robots and servers. This architecture includes a top-tier cloud server and an on-premises fog or edge server, enabling robots to offload computationally complex operations while maintaining minimal latency and maximal security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If computationally complex operations are performed locally on the robot, then operational autonomy and response time are improved, but device complexity and power consumption increase
Solution Approach 1:
The system segments computational tasks between the robot and server based on complexity. The robot handles time-critical local operations while offloading computationally intensive tasks to the server, dividing the computational burden into manageable segments that match each platform's capabilities
Solution Approach 2:
A communication interface acts as an intermediary between the robot and server, managing the offloading of computationally complex operations. This intermediary handles task distribution and data exchange, allowing the robot to maintain autonomy while leveraging server resources
2Device complexity
If computationally complex operations are offloaded to servers, then device complexity and power consumption are reduced, but network latency and security risks increase
Solution Approach 1:
Computational tasks are segmented by urgency and complexity. Time-critical operations remain local on the robot to avoid latency, while non-time-critical complex operations are offloaded to the server, ensuring that latency-sensitive functions are not impacted by network delays
Solution Approach 2:
Different computational operations are handled with different qualities - local operations prioritize speed and autonomy, while server operations prioritize computational power. The system applies appropriate handling strategies to different task types based on their specific requirements
3Productivity
If more compute resources are allocated to the robot, then operational efficiency is improved, but power consumption and heat generation increase
Solution Approach 1:
Computationally intensive operations are extracted from the robot and transferred to the server. This extraction removes the energy burden of complex computations from the mobile robot while maintaining the ability to perform those operations when needed
Solution Approach 2:
The communication interface serves as an energy-efficient intermediary, allowing the robot to access server compute resources without directly bearing their power consumption. The robot sends requests and receives results through this low-energy communication channel
Data Source
AI summary
The present disclosure relates to a multi-tiered computing environment for balancing compute resources in support of robot operations. In an example, a first server can receive from a robot located on a premises, a request and a first data, The first server can access a local configuration table to determine a program code associated with the first operation. The first server can generate second data based on the first data and the local configuration table. The first server can transmit the second data to the robot. The first server can receive, from the robot, third data indicating performance of the operation. The first server can transmit to a second server associated with the airplane model, fourth data, the fourth data generated based on the third data.