Manipulator execution path planning method for complex space guided measurement task

Patent metadata

Field	Value
Publication number	US-2026027715-A1
Application number	US-202519342659-A
Country	US
Kind code	A1
Filing date	Sep 28, 2025
Priority date	Jan 13, 2025
Publication date	Jan 29, 2026
Grant date	—

Abstract

A manipulator execution path planning method for a complex space guided measurement task, includes: determining a measurement space of a manipulator and a starting point and a target point of a path; determining an obstructed space and a free space of the measurement space and a proportion of the obstructed space in the measurement space using a collision algorithm, and classifying and constructing a space compression model according to the proportion of the obstructed space in the measurement space to compress the measurement space and remove an invalid space; finding candidate paths for connecting the starting point and the target point by constructing an adjacent node tree connected topology network; finding a required path in the candidate paths, and performing high-order curve fitting to obtain a final path; discretizing the final path, and solving joint angles of the manipulator to obtain a joint track.

First claim

What is claimed is:

1 . A manipulator execution path planning method for a complex space guided measurement task, comprising the following steps:
S 1 , determining a measurement space Ω of a manipulator; S 2 , determining a starting point and a target point of a manipulator path; S 3 , by means of a collision algorithm, determining an obstructed space Ω 0 and a free space Ω f of the measurement space Ω; and determining a proportion of the obstructed space in the measurement space; wherein a calculation formula for the proportion is expressed as:

λ
=

Ω
0

/
Ω

,

λϵ
(

0
,
1

]

;

S 4 , constructing a space compression model to compress the measurement space to obtain a compressed measurement space Ω n and remove an invalid space in the measurement space; wherein S 4 specifically comprises: when λ>0.5, constructing a first model to compress the measurement space; and when λ≤0.5, constructing a hyper-ellipsoidal model to compress the measurement space; wherein the first model is expressed as:

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⁢

or
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Assignees

• Univ Jiliang China

Inventors

• Luo Zai
• Jiang Wensong
• Wang Yan
• Yang Li
• Li Minyue
• Bian Dian
• Li Yaru

Classifications

• B25J9/1661

  characterised by task planning, object-oriented languages · CPC title

• B25J9/163

  learning, adaptive, model based, rule based expert control · CPC title

• B25J9/1666 Primary

  Avoiding collision or forbidden zones · CPC title

• B25J9/1664

  characterised by motion, path, trajectory planning · CPC title