Robotic Storage Path Selection Using Historical Command Data
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Solution Overview
Problem
Robotic storage systems face challenges in efficiently responding to varying client needs and maintaining performance due to differences in environmental setups and maintenance requirements, which can impact overall system efficiency and maintenance without disrupting host computer requests.
Innovation Solution
A self-directing robotic storage system that receives current commands and a history of prior commands to determine optimal paths for data retrieval and storage, allowing it to adapt and prioritize based on past performance, thereby enhancing operational efficiency and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a robotic storage system uses fixed control schemes for different environments, then the system structure remains simple, but the system cannot adapt to varying client needs and environmental differences, reducing productivity
Solution Approach 1:
The control scheme transitions from static to dynamic by continuously adapting to changing environmental conditions and client needs. The system monitors performance metrics and automatically adjusts control parameters in real-time, allowing the same robotic storage system to optimize its operation for different clients and workloads without requiring multiple fixed configurations.
Solution Approach 2:
The system performs self-configuration and self-optimization by automatically analyzing its own performance data and adjusting its control schemes accordingly. This self-service capability eliminates the need for manual reconfiguration when deploying to different environments, allowing the system to adapt autonomously to varying client needs while maintaining operational simplicity.
2Reliability
If maintenance is performed on the robotic storage system, then long term system health is improved, but host computer requests may be disrupted
Solution Approach 1:
The system performs maintenance activities in advance during periods of low utilization or predicted idle time, rather than waiting for failures or scheduling maintenance during peak demand. By proactively conducting maintenance when it least impacts productivity, the system ensures long-term reliability while minimizing disruption to host computer requests.
Solution Approach 2:
The system continuously monitors its own operational status, performance metrics, and utilization patterns to dynamically determine optimal maintenance timing. This feedback mechanism allows the system to automatically schedule maintenance activities that maximize reliability improvements while minimizing impact on productivity, adjusting maintenance schedules based on real-time system conditions and demand patterns.
Data Source
AI summary
Provided is a system and method for a robotic storage system. The system includes at least a first and second portable data element and at least a first and second data read/write device, structured and arranged to read portable data storage elements. A repository is structured and arranged to store the first and second portable data storage elements. At least one robot is structured and arranged to move a selected data storage element between the repository and a selected data read/write device. The system includes a history of prior commands, each prior command executed by a prior path selected from a group of optional paths. A receiver is structured and arranged to receive a current command for the robotic storage system, and a director is structured and arranged to direct the robotic storage system based on the current command and the history. An associated method is also provided.