A mechanical arm path planning method and system based on adaptive extended sampling

By adaptively expanding sampling and dynamically optimizing the robotic arm path planning, the problems of uneven sampling and slow convergence in existing algorithms under complex environments are solved, resulting in a smoother and more efficient path.

CN119388414BActive Publication Date: 2026-06-02ANHUI NORMAL UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
ANHUI NORMAL UNIV
Filing Date
2024-09-02
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing robotic arm path planning algorithms suffer from overly random sampling regions and inappropriate step size selection in complex environments, resulting in slow sampling rates, slow convergence speeds, and poor initial path quality.

Method used

An adaptive extended sampling-based robotic arm path planning method is adopted. The growth direction of the random tree is corrected by combining the adaptive extended sampling strategy and dynamic step size formula with the target gravity bias sampling strategy, and the path is optimized by the parent node reselection strategy of F-RRT*.

Benefits of technology

It improves the stability and accuracy of path planning, generates smoother paths, and reduces path length, number of samplings, and convergence time, which is consistent with the motion characteristics of actual mobile robots.

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Abstract

A mechanical arm path planning method and system based on adaptive expansion sampling belong to the technical field of path planning, and solve the problems of slow sampling rate, slow convergence speed and poor initial path quality caused by too random sampling area and unsuitable expansion step length selection when planning the path of the mechanical arm under the prior art; the adaptive expansion sampling strategy provided by the present application introduces a target bias force to correct the growth direction of the random tree based on the F-RRT* optimization algorithm, so that it is biased towards the target point, optimizes the inflection point, makes the generated path smoother, introduces the concept of target point gravity deviation, adaptively expands the strategy according to the complexity of obstacles in the environment, and expands with a dynamic step length, improves the stability and precision of the trajectory, and reduces the length of the generated path, the sampling times and the convergence time.
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