A planning method for connecting two-dimensional random closed graphs to generate the shortest path

Abstract

The invention discloses a planning method for connecting two-dimensional random closed figures to generate the shortest path. The method takes many two-dimensional closed figures randomly generated (the figures do not intersect and are far away), and uses the centroid of the figures as a data point set The basis of P sorting is to first determine a certain graphic centroid as the starting point, connect the remaining two-dimensional graphics one by one, optimize the path, take the shortest path as the target output, and use the improved greedy algorithm to obtain the optimal path; related parameters include path length L, point P i with P j the distance D between ij , point P i Total distance S to other points i , curve equation l. The invention realizes path planning of open-loop two-dimensional processing graphics, reduces time complexity, improves path planning optimality, and greatly improves processing efficiency compared with general intelligent algorithms.

Description

technical field [0001] The invention belongs to the technical field of numerical control machining, and in particular relates to a planning method for connecting a large number of two-dimensional closed figures to generate the shortest path in numerical control machining. Background technique [0002] In NC machining, path planning is an important part of data processing, and reasonable machining path planning can improve machining efficiency. For the processing path generated by CAM, the nearest neighbor search algorithm is generally used. Due to the short-sightedness of the algorithm, it is difficult to achieve global optimization. The solution time of the exact solution algorithm will increase exponentially with the complexity of the problem, which is not practical for practical complex problems. At present, the algorithm for two-dimensional machining trajectory planning generally adopts intelligent optimization algorithms, such as genetic algorithm, ant colony algorithm...

AI Technical Summary

Problems solved by technology

For the processing path generated by CAM, the nearest neighbor search algorithm is generally used. Due to the short-sightedness of the algorithm, it is difficult to achieve global optimization.

The solution time of the exact solution algorithm will increase exponentially with the complexity of the problem, which is not practical for practical complex problems

At present, the algorithm for two-dimensional machining trajectory planning generally adopts intelligent optimization algorithms, such as genetic algorithm, ant colony algorithm, annealing algorithm, etc. These algorithms are effective in specific applications, but the time complexity is high, and they are easy to fall into local optimum. Excellent, there may be a lot of difference from the optimal solution, and the solution effect is not very ideal

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