Weighted Trajectory Querying for Consistent Logistics Path Prediction
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Solution Overview
Problem
Existing logistics systems struggle with inconsistent trajectory predictions for devices like forklifts and autonomous mobile robots, leading to inefficient and potentially unsafe decision-making due to uncertainty in determining the expected trajectory, especially when changes in position and path occur.
Innovation Solution
A system that queries a trajectory database to determine an anticipated trajectory by generating typical trajectories from historical data, weighting similarity scores based on device attributes, and using sensor data to robustly predict the current path, ensuring temporal coherence and accurate decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional trajectory querying methods are used, then the system can retrieve trajectory data, but the trajectory prediction is inconsistent and uncertain especially when changes in position and path occur
Solution Approach 1:
The patent segments the trajectory comparison process into distinct phases: identifying candidate trajectories from historical data, computing similarity scores for each candidate, and selecting the best match. This segmentation allows for more precise control and reduction of uncertainty at each stage, improving overall prediction consistency.
Solution Approach 2:
The patent transforms the trajectory matching problem by changing parameters from simple positional matching to weighted similarity scoring that considers multiple attributes including position, speed, orientation, and device-specific characteristics. This parameter transformation enables more reliable and consistent trajectory predictions by capturing nuanced patterns in device movement.
2Speed
If real-time sensor data is used for trajectory determination, then the system can monitor current position and path, but logistics operations still lag behind real-time conditions
Solution Approach 1:
The patent performs preliminary actions by pre-processing historical trajectory data into structured formats and pre-computing similarity metrics. When real-time queries occur, the system can quickly match current sensor data against pre-processed historical patterns, significantly reducing decision-making delay while maintaining real-time responsiveness.
3Measurement precision
If device attributes are incorporated into trajectory querying, then the system can improve prediction accuracy, but the complexity of the querying system increases
Solution Approach 1:
The patent creates a universal trajectory querying framework that handles multiple device types (autonomous mobile robots, forklifts, etc.) through a single unified system. The weighted similarity scoring mechanism universally accommodates various device attributes and sensor configurations, improving prediction accuracy across different device classes without requiring separate specialized systems for each device type.
Data Source
AI summary
Querying trajectories is disclosed. A database of rich typical trajectories, which includes both position and attribute information for each of the typical trajectories, is created. Using a current trajectory of a node or a current position of the node, the database is queried for a rich typical trajectory that likely corresponds to the current or anticipated trajectory of the node. The database is searched by identifying candidate trajectories and determining a similarity score for each of the candidate trajectories. The best scoring candidate trajectory may be returned and logistics operations may be performed using the best scoring candidate trajectory.