A method and system for regional route collaborative scheduling of logistics drones

By constructing a drone monitoring platform and a force field simulation obstacle avoidance model, the problems of computational complexity and communication bandwidth bottlenecks in large-scale logistics drone collaborative scheduling were solved, enabling efficient and real-time drone collaborative flight and obstacle avoidance.

CN121903326BActive Publication Date: 2026-05-26SHENZHEN ZHIHUI QICE TECH CO LTD +1
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Patent Information

Application Number
CN202610368757.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-03-25
Publication Date
2026-05-26
Estimated Expiration
2046-03-25

AI Technical Summary

Technical Problem

In existing technologies, large-scale logistics drone regional route collaborative scheduling relies on centralized optimization algorithms, which leads to an explosive increase in the computational complexity of the central server and faces real-time computing power pressure and communication bandwidth bottlenecks.

Method used

By constructing a drone monitoring platform and utilizing a force field simulation obstacle avoidance model, distributed computing and autonomous obstacle avoidance can be achieved based on the drone's flight information and mission level, thereby reducing computational complexity and improving robustness.

Benefits of technology

It enables efficient and real-time obstacle avoidance for large-scale UAV cooperative flight, reduces the computational load on the central processing unit, and improves the robustness and scalability of the system.

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Abstract

This invention relates to the field of drone scheduling technology, and particularly to a method and system for regional route collaborative scheduling of logistics drones. The method includes: constructing a drone monitoring platform; determining whether flight interference exists based on a preset minimum interference distance, acquiring flight information of drones involved in the interference, and sending it to the corresponding drones; calculating yaw information for each drone based on the flight information and a built-in force field simulation obstacle avoidance model, and controlling the drones to fly based on the yaw information; when the distance between drones is greater than the minimum interference distance, outputting return-to-home information based on the force field simulation obstacle avoidance model, and controlling the drones to return to their flight paths. This invention provides a regional route collaborative scheduling method for logistics drones that treats each drone as an intelligent agent autonomously moving in a three-dimensional force field, decoupling complex global path planning from real-time obstacle avoidance, and greatly reducing the dimensionality and complexity of distributed computing.
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