A method, device and medium for drone path planning in logistics delivery

CN122306065APending Publication Date: 2026-06-30JINAN GOLDENWORLD HIGHWAY INDUSTRY DEVELOPMENT CO LTD

Patent Information

Authority / Receiving Office: CN · China
Patent Type: Applications(China)
Current Assignee / Owner: JINAN GOLDENWORLD HIGHWAY INDUSTRY DEVELOPMENT CO LTD
Filing Date: 2026-02-28
Publication Date: 2026-06-30

AI Technical Summary

Technical Problem

In urban high-density logistics delivery scenarios, drone swarms have low delivery efficiency and security. Existing centralized planning is computationally complex and not scalable, while fully distributed decision-making lacks global coordination, resulting in insufficient overall efficiency and security.

Method used

By constructing a dynamic environment model through multi-dimensional fusion of geographic and environmental information, a global coordination agent is used to generate a drone allocation scheme and cluster collaboration rules. Combined with a local execution agent to generate a local real-time situation map, path planning is performed and risk assessment is superimposed to select the optimal delivery route.

Benefits of technology

It provides a unified environmental perception foundation, proactively responds to dynamic factors, reduces conflicts between drones, ensures global collaboration and high robustness, and improves the safety and efficiency of the delivery process.

✦ Generated by Eureka AI based on patent content.

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Abstract

This application discloses a method, device, and medium for drone path planning in logistics delivery. Belonging to the field of logistics delivery technology, it addresses the problem of low overall delivery efficiency and safety of drone swarms in existing logistics delivery methods when facing high-density urban logistics delivery. The method includes: multi-dimensionally fusing geographical and environmental information corresponding to the logistics delivery area to construct a dynamic environment model; generating drone allocation schemes and swarm coordination rules for the logistics delivery task through a global coordination agent based on the global state information and dynamic environment model corresponding to the logistics delivery task; analyzing the received allocation schemes and swarm coordination rules through a local execution agent to generate a reference execution trajectory, and generating a drone delivery strategy based on the reference execution trajectory and a local real-time situation map; and superimposing risks onto each path in the drone delivery strategy, determining the optimal delivery path based on the superposition result.

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