Whole-body obstacle avoidance motion planning method for flight hoisting system based on hyperplane

By proposing a hyperplane-based whole-body obstacle avoidance motion planning method for flight hoisting systems, the problem of whole-body obstacle avoidance in unknown dynamic environments for multi-rotor UAV sling transport systems was solved, enabling real-time planning and safe flight, and improving the system's perception capabilities and safety.

CN122151875APending Publication Date: 2026-06-05NANKAI UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
NANKAI UNIV
Filing Date
2026-01-23
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing technologies struggle to achieve full obstacle avoidance for multi-rotor drone tethered transport systems in unknown or dynamic environments, especially in environments with unknown or dynamic obstacles. Traditional methods cannot guarantee the safety and real-time planning of the tether and load.

Method used

A whole-body obstacle avoidance motion planning method based on a hyperplane is adopted for a flying hoisting system. By acquiring obstacle point clouds and load trajectories, a separable hyperplane is constructed for whole-body collision constraints. The flight trajectory is then optimized by combining a cost function to ensure the safety of the load, hoisting rope, and aircraft.

Benefits of technology

It achieves real-time full-body obstacle avoidance for multi-rotor UAV tethered transport systems in unknown dynamic environments, improving perception capabilities and safety redundancy, reducing computational complexity, and ensuring trajectory smoothness and mission completion accuracy.

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Abstract

The present application relates to the field of unmanned aerial vehicle control, and provides a full-body obstacle avoidance motion planning method for a flight hoisting system based on a hyperplane, including obtaining an obstacle point cloud, obtaining an obstacle point set and an obstacle historical trajectory from the obstacle point cloud, and obtaining an obstacle predicted trajectory from the obstacle point set and the obstacle historical trajectory; obtaining a load trajectory and a multi-rotor trajectory, constructing a separable hyperplane from the obstacle predicted trajectory, the load trajectory and the multi-rotor trajectory, and obtaining a full-body collision constraint using the separable hyperplane; obtaining an alternative velocity vector from the obstacle predicted trajectory and under the constraint of the full-body collision constraint, performing priority sorting on the alternative velocity vector to obtain an initial flight trajectory; establishing a cost function, optimizing the initial flight trajectory under the constraint through the cost function to obtain a target flight trajectory, and flying according to the target flight trajectory.
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