Robotic Architecture Mapping for Low-Latency Task Allocation
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Solution Overview
Problem
Real-time robotic systems, such as autonomous vehicles, face challenges in optimizing system performance due to high latency, which is critical for meeting safety and responsiveness requirements, as current approaches primarily focus on resource allocation rather than latency optimization.
Innovation Solution
The method involves generating a model that maps software and hardware graphs to allocate computational tasks effectively, optimizing the robotic system architecture to meet latency requirements by assigning nodes and edges to hardware components, and incorporating latency into an objective function to minimize system latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional trial-and-error processes are used for system design, then hardware and software components can be connected, but the design process becomes laborious and latency optimization becomes intractable
Solution Approach 1:
The patent transforms the design optimization problem by changing parameters from traditional resource allocation metrics to latency-centric metrics. The system models latency as a function of computational task assignments and communication data flow, enabling direct optimization of system responsiveness rather than treating it as a secondary concern after resource allocation.
Solution Approach 2:
The patent replaces manual trial-and-error design processes with an automated computational optimization system. The system uses mathematical modeling and algorithmic optimization to automatically determine optimal task-to-component mappings and data flow configurations, substituting human iterative design with automated computational methods that can evaluate numerous configurations systematically.
2Productivity
If current resource allocation approaches are used, then memory usage and bandwidth utilization are optimized, but latency requirements are not addressed
Solution Approach 1:
The patent segments the robotic system into distinct computational tasks and hardware components, modeling each with specific latency characteristics. By breaking down the system into discrete task nodes and communication edges with measurable latency properties, the system can identify and optimize critical paths that contribute most to overall system latency while maintaining resource allocation efficiency.
Solution Approach 2:
The patent adds a temporal dimension to traditional resource allocation by incorporating latency as an explicit optimization parameter. Rather than optimizing only spatial resource distribution (which components handle which tasks), the system simultaneously optimizes temporal characteristics (how long tasks take and how data flows between them), creating a multi-dimensional optimization framework that addresses both resource efficiency and responsiveness.
3Adaptability or versatility
If more hardware components and computational tasks are added to robotic systems, then system functionality improves, but latency optimization becomes more difficult
Solution Approach 1:
The patent creates a universal optimization framework that can handle varying numbers of hardware components and computational tasks through a standardized mathematical model. The latency model and optimization algorithm are designed to accommodate different system configurations without requiring fundamental changes, allowing the same framework to optimize systems with different component counts and configurations.
Solution Approach 2:
The system incorporates feedback mechanisms where the optimization model uses measured or estimated latency data from the robotic system to iteratively improve task assignments and data flow configurations. By continuously monitoring actual system latency and adjusting the optimization parameters accordingly, the system can adapt to changes in component count and configuration while maintaining optimal performance.
Data Source
AI summary
Systems and methods for designing a robotic system architecture are disclosed. The methods include generating a model that defines one or more requirements for a robotic device for a mapping between a software graph and a hardware graph. The model is used for allocating a plurality of computational tasks in a computational path included in the software graph to a plurality of hardware components of the robotic device to yield a robotic system architecture. The methods also include using the robotic system architecture to configure the robotic device to be capable of performing functions corresponding to the software graph, where the robotic system architecture is optimized to meet one or more latency requirements.