Crane Hoisting Path Planning Using Segmented A-Star Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current hoisting path planning methods are inefficient due to reliance on large volumes of data, requiring significant computational resources and auxiliary personnel, and are heavily dependent on the crane driver's expertise.
Innovation Solution
A method that divides the hoisting path into upper vehicle body and lower vehicle body path planning, using a crane model and A-star algorithm to generate raster graphic data for each, reducing data volume and improving efficiency by decoupling calculations and using the A-star algorithm for optimized path planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hoisting path planning relies on a huge amount of hoisting system data, then the completeness of path planning is improved, but the efficiency of path search deteriorates
Solution Approach 1:
The patent divides the hoisting system configuration space into upper vehicle body configuration space and lower vehicle body configuration space. The upper vehicle body includes the main arm and hook mechanism, while the lower vehicle body includes the walking mechanism. This segmentation allows independent processing of each subsystem, reducing the overall data volume and computational complexity while maintaining comprehensive path planning coverage.
2Measurement precision
If traditional path planning methods process large volumes of data, then planning accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the path planning process into upper vehicle body path planning and lower vehicle body path planning. Each segment is processed independently using A-star algorithm, which reduces the search space and computational time while maintaining planning accuracy through separate configuration space models for each vehicle body.
Solution Approach 2:
The patent performs preliminary action by pre-dividing the hoisting path into upper and lower vehicle body paths and pre-processing their respective configuration space models. This preliminary segmentation and model preparation enables faster real-time path planning execution by avoiding the need to process the entire system configuration space simultaneously.
Data Source
AI summary
Disclosed are a hoisting path planning model construction method, a hoisting path planning method and a crane. The hoisting path planning model construction method includes: building a crane model; constructing a hoisting system configuration space model based on a current operation scenario and the crane model, and the hoisting system configuration space model includes upper vehicle body data of a crane and lower vehicle body data of the crane; aiming at the hoisting system configuration space model and the upper vehicle body data, generating upper vehicle body raster graphic data of the crane; and aiming at the hoisting system configuration space model and the lower vehicle body data, generating lower vehicle body raster graphic data of the crane; and using an A-star algorithm and combining the upper vehicle body raster graphic data and the lower vehicle body raster graphic data to construct a hoisting path planning model.