An improved RRT behavioral planning method for robots based on PTM algorithm

Abstract

For multi-dimensional high-order nonlinear systems with unstable path quality in parameter and extension terms, we developed a new fast search random tree strategy. First, we established a high-order Lipschitz vector field dynamic system to adapt to high-order systems of multi-degree-of-freedom robots, with the complex obstacle function being one of its key components. Secondly, we designed a classification gap filtering network layer (Classification LSTM) to screen training data models and ensure the global stability of data in path design. Additionally, the visual sensors deployed in the unit area effectively implement the path marking backtracking strategy and dead zone path simplification. Finally, three examples are provided to verify the effectiveness of this design method.