Asynchronous Motion Primitives for Edge Robot Coordination
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Solution Overview
Problem
Existing technologies face challenges in effectively controlling high-frequency, low-level robot motions and coordinating them across networks with unreliable communication channels, particularly in large-scale robot deployments where latency and throughput variability are significant issues.
Innovation Solution
The implementation of Asynchronous Adaptive Motion Primitives (A2MPs) organized into task graphs, which utilize stochastic and discrete events for hierarchical partitioning and dynamic trajectory management, ensuring flexible, scalable, and seamless integration with lightweight computational resources, and incorporating non-deterministic adaptation for communication channel variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional synchronous control methods are used for robot coordination, then control precision can be maintained, but communication latency and network variability cause coordination failures in large-scale deployments
Solution Approach 1:
The control system is segmented into hierarchical levels: high-level task orchestration separates from low-level motion control. Each robot operates autonomously at low level while coordinating at high level, eliminating the need for synchronous communication across the entire control stack and reducing latency impact on coordination reliability
Solution Approach 2:
The system transitions from static synchronous control to dynamic asynchronous control where robots can execute motions independently based on local state and pre-planned trajectories. The control rate adapts to individual robot needs rather than being constrained by network synchronization requirements, improving reliability while reducing communication time loss
2Manufacturing precision
If high control rates (above 1 kHz) are implemented for industrial robots, then motion precision improves, but communication channel limitations prevent effective coordination
Solution Approach 1:
Control functions are segmented by frequency requirements: high-rate local feedback loops maintain motion precision independently, while low-rate high-level coordination handles task-level synchronization. This segmentation allows each layer to operate at optimal rates without being constrained by communication bandwidth limitations
Solution Approach 2:
Pre-planned trajectories and motion primitives act as intermediaries between high-level task commands and low-level actuation. The high-level controller generates reference trajectories offline, allowing the low-level controller to execute precise motions using local feedback without requiring continuous high-rate communication for trajectory updates
3Device complexity
If centralized control architecture is used for robot orchestration, then coordination can be simplified, but scalability is limited in large-scale deployments
Solution Approach 1:
The control architecture is segmented into distributed autonomous agents at the robot level and a high-level orchestration layer. Each robot maintains independent control capabilities, allowing the system to scale by simply adding more autonomous agents without increasing centralized controller complexity proportionally
Solution Approach 2:
Each robot possesses self-service capabilities through autonomous low-level control and local state estimation. Robots can execute tasks independently using pre-planned trajectories and local feedback, reducing dependency on centralized control and enabling scalable deployment where each unit manages its own operations while coordinating through standardized high-level interfaces
Data Source
AI summary
A component of an edge server, including: processor circuitry; and a non-transitory computer-readable storage medium including instructions that, when executed by the processor circuitry, cause the processor circuitry to: distribute, to a client device, tokens that enable its end device to execute respective asynchronous adaptive motion primitives (A2MPs) of a task graph of a task, wherein an A2MP is a motion primitive of encoded motion factoring in motion updates from the end device; receive A2MP task execution status messages during execution of the A2MPs; and dynamically update the distribution of the token or the task graph based on the A2MP task execution status messages to modify a trajectory of the end device.