Automatic Path Planning and Optimization of Sheet Metal Bending Robot Based on Knowledge and Improved PRM Algorithm
An initial path node generation method based on empirical knowledge is proposed for the in-ter-task cell motion planning problem during offline programming simulation of sheet metal bending ro-bots.The sampling model is constructed based on the initial path nodes as body centres,and the sampling method of the original PRM algorithm is improved by obtaining model vertices and body centres as sam-pling points.By constructing the sampling model through several iterations,the algorithm path has asymp-totic optimality,achieving automatic planning and optimization of the path of the sheet metal bending ro-bot.Finally,the feasibility and efficiency of the algorithm are verified by the simulation experiment of the sheet metal bending robot.
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