Edge In-Memory Datastore for Logistics Robot Throughput
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Solution Overview
Problem
Existing logistics center systems face limitations in throughput and operational performance due to constrained computing resources, leading to slowed processes, dropped tasks, and decreased performance, especially during peak hours when communication exceeds transaction limits.
Innovation Solution
Implementing an in-memory datastore at the edge computing resources within logistics centers to store information about robotic agents' states, tasks, and communications, allowing for increased throughput and reduced latency by enabling local data processing and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computing resources are located remotely from the logistics center, then centralized control is achieved, but throughput is limited and latency increases
Solution Approach 1:
The patent segments the computing architecture into two parts: a remote computing resource for centralized coordination and an edge device located within the logistics center for local task management. This segmentation allows tasks to be processed locally at the edge device, increasing throughput and reducing latency while maintaining centralized oversight for complex decisions and resource allocation.
Solution Approach 2:
The edge device acts as an intermediary between the remote computing resource and the robotic agents within the logistics center. It receives task allocations from the remote resource, manages local task execution, and reports status back, thereby reducing communication overhead and latency while maintaining system-wide coordination.
2Productivity
If more robotic agents are assigned to workstations, then operational performance improves, but system complexity increases
Solution Approach 1:
The edge device provides self-service capabilities by autonomously managing task allocation, monitoring robotic agent states, and handling local coordination without requiring constant communication with the remote computing resource. This reduces system complexity while enabling more robotic agents to operate efficiently through local intelligence.
Solution Approach 2:
The system implements partial centralization by keeping only critical functions (resource allocation, complex decision-making) at the remote computing resource, while delegating routine task management and monitoring to the edge device. This partial action approach simplifies the overall system architecture while supporting a larger number of robotic agents.
Data Source
AI summary
A local device may receive, from a remote device, a task to be completed at a workstation within a logistics center in which orders are fulfilled and processed. The local device may determine, among a plurality of robotic agents at the workstation, a robotic agent configured to perform the task. An in-memory datastore having cache and compute capabilities may be stored in memory of the local device may store data associated with the task and the robotic agent to complete the task. The local device may instruct the robotic agent to perform the task and may receive, from the robotic agent, an indication associated with the performance of the task. This data may be stored in the in-memory datastore and/or may be used for assigning future tasks.