Method, apparatus and equipment for generating collaborative trajectories of multiple intelligent terminals in ports

By combining dynamic obstacle spatiotemporal maps and conditional diffusion models with Pareto front optimization and hierarchical game strategies, the real-time trajectory planning problem of multiple intelligent terminals in port operations with human-machine hybrid scenarios was solved. This achieved safe and efficient multi-objective constraint balance and human-machine collaborative decision-making, improving port operation efficiency and safety.

CN120972956BActive Publication Date: 2026-07-03CCCC MECHANICAL & ELECTRICAL ENG
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CCCC MECHANICAL & ELECTRICAL ENG
Filing Date
2025-08-29
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

Traditional methods struggle to cope with the real-time dynamic changes of multiple intelligent terminals in port operations involving human and machine collaboration. They suffer from high computational complexity, lack theoretical basis for multi-objective trade-offs, and the inability of existing methods to generate multimodal candidate trajectories, resulting in insufficient planning flexibility.

Method used

By collaboratively generating a dynamic obstacle spatiotemporal map and a conditional diffusion model, combined with Pareto front multi-objective optimization and hierarchical game strategy, real-time trajectory planning and multi-objective constraint balance are achieved, generating interpretable collision avoidance strategies.

Benefits of technology

It has achieved a significant improvement in port operation efficiency and safety. Through dynamic obstacle modeling, multi-objective optimization and human-machine intent quantification, it ensures safe and efficient trajectory planning for the terminal in complex scenarios.

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Abstract

This application provides a method for generating collaborative trajectories of multiple intelligent terminals in ports, relating to the field of artificial intelligence technology. This method achieves safe, efficient, and interpretable trajectory planning for multiple intelligent terminals in ports under complex scenarios by collaboratively generating dynamic obstacle spatiotemporal maps and conditional diffusion models, combined with Pareto front multi-objective optimization and hierarchical game strategies, real-time trajectory planning, multi-objective constraint balancing, and human-machine collaborative decision-making, significantly improving port operation efficiency and safety.
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Citation Information

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