Multi-objective trajectory planning for industrial robots based on machine learning
In order to meet the diverse needs of robot work trajectories,a new multi-objective sparrow search algorithm with opti-mization objectives of time,energy and impact was proposed to find the optimal trajectory of robots.Firstly,the joint space trajecto-ry was constructed by the 7-degree B-spline interpolation method,and the multi-objective synthetic optimal trajectory planning model was established.Secondly,the sparrow search algorithm was improved by constraint violation computing,non-dominated ranking and elitist retention to solve the multi-objective trajectory planning problem.Finally,the data in the random population was deleted by the bag-tree classification algorithm,and a five-layer BP neural network was built to replace and improve the numerical calculation part of the fitness value in the multi-objective sparrow search algorithm,thus improving the efficiency of the algorithm.Through the MATLAB simulation and experiment,the feasibility and validity of the algorithm were demonstrated.
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