Autonomous Mobile Picking With Visual Receptacle Guidance
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Solution Overview
Problem
Order picking in warehouses is labor-intensive, and existing methods lack efficiency in automating the process while allowing for flexible scaling of labor and capital equipment.
Innovation Solution
The implementation of autonomous mobile robotic units capable of autonomous operation and augmentation by humans, equipped with visual indicators and robotic arms, that can alter their path to work alongside personnel, facilitating efficient picking and putting of articles in an order fulfillment facility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If autonomous mobile robotic units are deployed for order picking, then labor costs are reduced and automation extent increases, but device complexity and initial capital investment increase
Solution Approach 1:
The system divides the autonomous picking function into separate mobile robotic units, each capable of independent operation. Each unit is equipped with its own receptacles, indicators, and navigation capabilities, allowing the system to achieve high automation through multiple simple units rather than one complex centralized system.
Solution Approach 2:
The mobile robotic units are designed with universal functionality to perform multiple tasks: they can autonomously navigate to pick stations, receive visual indicators for picking decisions, transport receptacles containing selected items, and dock at consolidation locations. This multi-functionality reduces overall system complexity by using standardized units for diverse operations.
2Productivity
If robotic units are equipped with visual indicators and robotic arms for autonomous operation, then picking productivity increases, but device complexity and manufacturing cost increase
Solution Approach 1:
The robotic units operate autonomously without requiring complex external control systems. Each unit independently receives visual indicators at pick stations, makes picking decisions based on displayed information, executes the picking action, and navigates to appropriate destinations. This self-service capability increases productivity while keeping individual unit complexity manageable.
Solution Approach 2:
The system replaces complex mechanical control systems with visual indicator systems. Instead of using complex mechanical signaling or programming interfaces, the patent uses visual indicators (such as lights or displays) to communicate picking instructions to the robotic units, simplifying the control mechanism while maintaining high productivity.
3Adaptability or versatility
If robotic units can alter their planned path to function in the presence of persons, then adaptability and ease of operation improve, but control system complexity increases
Solution Approach 1:
The robotic units incorporate sensing capabilities that detect the presence of persons or obstacles in their planned path. When persons are detected, the units receive feedback and automatically alter their navigation to avoid collisions. This feedback mechanism enables adaptable coexistence with human workers without requiring overly complex path planning algorithms.
Solution Approach 2:
The path planning system is designed to be dynamic rather than static. The robotic units can continuously adjust their planned paths in real-time based on changing environmental conditions, such as the presence of human workers or moved objects. This dynamic adaptability allows flexible operation in mixed human-robot environments while keeping the control system manageable through incremental adjustments.
4Productivity
If multiple robotic units operate autonomously in the same facility, then productivity and capacity scale increase, but coordination complexity and potential conflicts increase
Solution Approach 1:
The system segments the facility into distinct operational zones and assigns specific tasks to different robotic units. Each unit operates semi-independently within its assigned functions (e.g., some units specialize in picking, others in transport), reducing coordination complexity while maintaining high overall productivity through parallel operation of multiple specialized units.
Data Source
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AI summary
An order-picking method includes autonomously routing a plurality of mobile robotic units in an order fulfillment facility and picking articles to or putting articles from the robotic units in the order fulfillment facility. A material-handling robotic unit that is adapted for use in an order fulfillment facility includes an autonomous mobile vehicle base and a plurality of article receptacles positioned on the base. A visual indicator associated with the receptacle facilitates picking articles to or putting articles from the robotic unit.