Warehouse Drone Path Prediction for Obstacle-Free Rack Shortcuts
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Solution Overview
Problem
Existing warehouse management systems face challenges in determining obstacle-free paths for drones to navigate through warehouses without colliding with inventory moving devices, which often requires drones to fly slower and waste time finding alternative routes.
Innovation Solution
The system determines obstacle-free paths for drones by receiving status updates on inventory moving tasks, predicting the locations of inventory moving devices, and identifying shortcuts through storage racks, allowing drones to avoid collisions and optimize their routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If drones rely on collision avoidance technology to avoid inventory moving devices, then collision safety is improved, but drone flight speed decreases significantly
Solution Approach 1:
The system performs preliminary actions by predicting the future locations of inventory moving devices before the drone reaches those areas. The warehouse management system receives status information about inventory moving tasks, predicts where forklifts and other devices will be, and pre-calculates safe flight paths. This allows the drone to maintain higher speeds while still avoiding collisions, as it is reacting to predicted positions rather than reacting slowly to real-time detected obstacles.
2Reliability
If drones use real-time collision detection to avoid obstacles, then safety is improved, but time is wasted determining alternative routes
Solution Approach 1:
The system calculates multiple potential flight paths in advance, before the drone actually needs them. By predicting the movements of inventory moving devices and pre-determining alternative routes based on these predictions, the system ensures that safe paths are already identified when the drone approaches potential conflict zones. This eliminates the time delay that would occur if alternative routes had to be calculated in real-time during flight.
Solution Approach 2:
The system continuously receives status information from inventory moving devices and updates predictions accordingly. This feedback loop allows the system to monitor the actual positions and tasks of forklifts and other devices, compare them against predicted positions, and dynamically adjust flight paths as needed. The feedback mechanism ensures that the drone maintains safety while minimizing deviations from its original route.
3Reliability
If drones fly slower to avoid detected obstacles, then collision risk is reduced, but inventory validation productivity decreases
Solution Approach 1:
By predicting obstacle locations in advance and pre-calculating safe flight paths, the system allows drones to maintain higher speeds throughout most of their flight. The drone only needs to deviate from its optimal path when approaching predicted obstacle locations, rather than flying slowly throughout the entire warehouse. This significantly improves productivity while maintaining collision risk reduction.
Data Source
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AI summary
Systems and methods are provided for determining obstacle-free paths of an areal vehicle in a warehouse and for determining shortcuts to be generated in the warehouse to increase the number of obstacle-free paths available to the areal vehicle. The systems and methods include predicting at least one location of an inventory moving device in the warehouse based on the status of received inventory moving tasks, and determining an obstacle-free path of an aerial vehicle to a desired location in the warehouse based on the predicted at least one location of the inventory moving device. The systems and methods also include receiving inventory validation tasks in a warehouse comprising a plurality of storage racks, requesting, based on the received inventory validation tasks, a shortcut to be generated in a storage rack of the plurality of storage racks to provide a path segment for performing the inventory validation task.