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Summary

This summary is machine-generated.

Researchers developed a novel fast search random tree strategy for unstable high-order nonlinear systems. This method enhances path planning stability and efficiency for robotic systems, validated through simulations.

Keywords:

Classification gap filtering network layer Complex obstacle function Dead zone path simplification High-order Lipschitz vector field dynamic system Path marking backtracking strategy

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Area of Science:

Robotics and Control Systems
Nonlinear Dynamics
Artificial Intelligence in Path Planning

Background:

High-order nonlinear systems often exhibit unstable path quality, posing challenges for robotic applications.
Existing path planning methods struggle with complex obstacle environments and ensuring global data stability.

Purpose of the Study:

To introduce a new fast search random tree strategy for improved path planning in multi-dimensional high-order nonlinear systems.
To enhance the stability and efficiency of path design for multi-degree-of-freedom robots operating in complex environments.

Main Methods:

Development of a high-order Lipschitz vector field dynamic system to model robot dynamics.
Implementation of a Classification LSTM (Long Short-Term Memory) network layer for data screening and global stability assurance.
Integration of visual sensors for path marking, backtracking, and dead zone simplification.

Main Results:

The proposed strategy effectively addresses unstable path quality in high-order nonlinear systems.
The Classification LSTM layer successfully screens training data, ensuring global stability in path design.
Visual sensor integration facilitates robust path marking and simplification strategies.

Conclusions:

The novel fast search random tree strategy offers a significant advancement in robotic path planning for complex nonlinear systems.
The method demonstrates effectiveness in enhancing path stability, efficiency, and adaptability to dynamic environments.