Networked Robotic Manipulators for Shared Manipulation Profiles
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Solution Overview
Problem
Existing robot stations in inventory systems often struggle to efficiently share knowledge and improvements in task performance, leading to suboptimal overall system efficiency due to the lack of effective data sharing and propagation of best practices between stations.
Innovation Solution
A system where robot stations are connected to a central station over a network, allowing for the collection and sharing of manipulation data, generation of manipulation profiles, and updates, enabling the propagation of collective knowledge on how to manipulate objects across the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robot stations operate independently with individual task performance methods, then each station maintains operational autonomy and simplicity, but the overall system efficiency is suboptimal due to inability to share knowledge and improvements
Solution Approach 1:
The patent combines multiple independent robot stations into a networked system where manipulation data from all stations is collected and aggregated. The central station merges this data to generate comprehensive manipulation profiles that are then distributed back to individual stations, enabling knowledge sharing while maintaining operational independence.
Solution Approach 2:
The manipulation profiles generated by the central station serve multiple robot stations simultaneously. A single profile can be applied across different stations performing similar tasks, making the system universally applicable and eliminating redundant learning processes at each individual station.
2Reliability
If manipulation data is collected and processed centrally to generate manipulation profiles, then knowledge propagation across the system is achieved, but system complexity and data processing requirements increase
Solution Approach 1:
The central station acts as an intermediary between individual robot stations. It collects raw manipulation data, processes it into standardized profiles, and distributes these profiles back to stations. This intermediary structure simplifies the overall architecture by centralizing complex processing tasks while keeping individual stations simple and modular.
Solution Approach 2:
Instead of having each robot station independently learn and develop manipulation strategies through trial and error, the system creates standardized manipulation profiles that are copied and distributed to all stations. This copying approach ensures consistent task performance across the system while reducing the computational burden on individual stations.
3Productivity
If robot stations implement individual learning and adaptation mechanisms, then each station can optimize its own performance, but the time required for system-wide efficiency improvement is extended
Solution Approach 1:
The central station proactively collects manipulation data from all robot stations and generates optimized manipulation profiles in advance. These profiles are then distributed to stations before they encounter similar tasks, allowing stations to perform optimally from the start rather than learning through time-consuming trial and error.
Solution Approach 2:
The system implements a feedback loop where manipulation data from robot stations is continuously collected, analyzed, and used to update manipulation profiles. These updated profiles are redistributed to stations, creating a continuous improvement cycle that rapidly propagates efficiency gains across the entire system.
Data Source
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AI summary
Robotic manipulators may be used to manipulate objects. Manipulation data about manipulations performed on objects may be generated and accessed. This data may be analyzed to generate a profile indicating how an object may be manipulated. A portion of the profile may be transmitted to a particular robotic manipulator. For example, the portion may be based on a manipulation capability of the robotic manipulator. In turn, the robotic manipulator may use the portion of the profile to manipulate the object.