Laboratory Sample Distribution System Dynamic Path Planning
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Solution Overview
Problem
Existing laboratory sample distribution systems face inefficiencies in calculating and executing the movement of sample container carriers, leading to potential interruptions and delays due to pre-calculated routes and lack of real-time adaptability.
Innovation Solution
A method that logically models the transport plane as a network of nodes with free and reserved time-windows, allowing for dynamic planning and execution of nonstop movement paths by analyzing reachability between nodes and reserving time-windows for uninterrupted carrier movement, enabling real-time adaptability and efficient path planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pre-calculated fixed routes are used for sample container carriers, then the system operation is simplified, but the reliability and efficiency of sample distribution deteriorates due to potential interruptions and delays
Solution Approach 1:
The patent implements dynamic route planning that adapts to real-time system state changes. Instead of static pre-calculated routes, the system continuously recalculates optimal paths based on current carrier positions, node availability, and time-window constraints, thereby maintaining reliability while managing complexity through automated dynamic adjustment
Solution Approach 2:
The system incorporates feedback mechanisms by monitoring the actual state of the transport plane and using this information to adjust route planning. The control device receives real-time data about carrier positions and node reservations, then modifies subsequent route calculations to prevent conflicts and ensure nonstop movement, resolving the contradiction between operational simplicity and distribution reliability
2Ease of operation
If pre-calculated fixed routes are used for sample container carriers, then the system operation is simplified, but the efficiency of sample distribution deteriorates due to interruptions and delays
Solution Approach 1:
The system employs dynamic route optimization that adapts to real-time conditions, allowing carriers to maintain nonstop movement by adjusting paths based on current node availability and time-window constraints, thereby improving distribution efficiency while maintaining automated operation
Solution Approach 2:
The system performs preliminary reservation of time-windows for nodes along planned routes before carriers actually traverse them. This advance reservation ensures that paths are secured in advance, preventing interruptions and delays while maintaining automated route management, thus improving efficiency without sacrificing operational simplicity
3Reliability
If the transport plane is logically modeled by a plurality of nodes with time-windows, then the reliability of movement planning is improved, but the device complexity increases
Solution Approach 1:
The patent segments the transport plane into discrete nodes with associated time-windows, creating a structured logical model. This segmentation allows for systematic route planning by breaking down the continuous transport space into manageable discrete units, improving reliability through structured planning while the modular nature keeps complexity manageable
Solution Approach 2:
The system creates a logical copy or virtual model of the transport plane using nodes and time-windows, rather than directly managing the physical complexity. This virtual representation simplifies the planning process by working with an abstracted model that captures essential dynamics without the full complexity of the physical system, thereby improving reliability without proportionally increasing actual device complexity
4Productivity
If dynamic route planning with time-window analysis is implemented, then the efficiency of sample distribution is improved, but the computational requirements and system complexity increase
Solution Approach 1:
The system segments the route planning problem into discrete node evaluations with specific time-window constraints. By breaking down the continuous planning problem into discrete atomic decisions about individual nodes and their time-windows, the system achieves dynamic optimization efficiency while managing computational complexity through structured decomposition
Solution Approach 2:
The system performs preliminary analysis and reservation of time-windows for nodes before finalizing carrier routes. This advance preparation of temporal constraints allows for efficient real-time route planning by having pre-evaluated availability information ready, thereby improving distribution efficiency while reducing the computational burden during actual route execution
Data Source
AI summary
A method of operating a laboratory sample distribution system is presented. The system comprises container carriers, a transport plane, and drive elements. The container carriers carry sample containers. The transport plane supports the container carriers. The drive elements move the container carriers on the transport plane. The method comprises planning a movement path for a container carrier from a start to a goal on the transport plane modelled by nodes. The nodes are free for one time-window or reserved for one-time window. The planning comprises analyzing the reachability out of a free time-window of one node to free time-windows of a next node and an over-next node such that planned movement of the container carrier is nonstop. The method comprises reserving the planned movement path comprising a sequence of time-windows of nodes and moving the container carrier along the reserved movement path on the transport plane by a drive element.