Tiered Robot Compute Balancing for Low-Latency Autonomous Operations
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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, distributing compute resources between robots and servers, allowing robots to perform computationally complex operations autonomously while offloading tasks to on-premises servers for reduced design complexity and improved agility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If computational tasks are offloaded to servers, then device complexity is reduced, but network latency increases
Solution Approach 1:
The system segments computational tasks into two categories: autonomous operations that require low latency and are executed locally on the robot, and non-time-critical operations that are offloaded to servers. This segmentation allows the robot to maintain agility for time-sensitive tasks while still benefiting from server resources for complex computations that do not require immediate response.
2Device complexity
If computational tasks are offloaded to servers, then device complexity is reduced, but network security risks increase
Solution Approach 1:
The system segments network communication into secure autonomous operations that occur locally without external network dependency, and non-critical operations that communicate with servers. This segmentation minimizes the attack surface for network security threats while still allowing the robot to benefit from server-based computational resources.
Solution Approach 2:
The robot is designed to autonomously perform critical operations without requiring network communication, making it self-sufficient for time-sensitive and security-critical tasks. This self-service capability reduces dependency on network infrastructure and mitigates security risks associated with constant network connectivity.
3Extent of automation
If compute resources are implemented on the robot, then autonomous operation capability is improved, but power consumption increases
Solution Approach 1:
The system segments computational workload between the robot's local processor and external servers. The robot maintains minimal local compute resources for autonomous navigation and immediate response tasks, while more intensive computational tasks are distributed to servers, thereby balancing autonomous capability with power consumption constraints.
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 robot is tasked with performing an operation associated with an airplane having an airplane model. To do so, the robot may need another operation that is computationally complex to be performed. An on-premises server can execute a process that corresponds to this computationally-complex operation based on sensor data of the robot and can output the resulting data to the robot. Next, the robot can use the resulting data to execute another process corresponding to its operation and can indicate performance of this operation to the on-premises network. The on-premises network can send the indication about the operation performance to a top-tier server that is also associated with the airplane model.