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A multi-objective trajectory planning method based on the improved immune clonal selection algorithm
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DOI：10.1016/j.rcim.2019.04.016.png)
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Abstract
En 中文
In this paper, a multi-objective trajectory planning method based on an improved immune clonal selection algorithm is proposed. On the basis of the inverse kinematic analysis and dynamic model of the 6-UPU parallel assembly platform (PAP), the motion trajectory of the mobile platform is planned in Cartesian space with a quintic B-spline curve. Three important technical indexes, i.e. assembly efficiency, production cost and motion stationarity, are selected to evaluate the performance of planned trajectory. Correspondingly, three ideal objective functions which can represent the aforementioned technical indicators, namely the functions respectively of the total motion time, of the energy consumed in the process of motion and of the maximum absolute value of actuator's jerk, are defined to establish the multi-objective model for trajectory optimization. Then an improved immune clonal selection algorithm (IICSA) is developed to solve the optimization problem. Results of trajectory simulation confirm that the demands of assembly task can be met. Thus, the proposed method is effective and universal and it has the potential to be referenced or applied directly to other types of manipulators.
Keywords:
Parallel assembly platform
Trajectory planning
B-spline curve
Multi-objective optimization
improved optimization algorithm
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