Robot manipulators perform a point-point task under kinematic and dynamic constraints.Due to multi-degree-of-freedom coupling characteristics,it is difficult to find a better desired trajectory.In this paper,a multi-objective trajectory planning approach based on an improved elitist non-dominated sorting genetic algorithm(INSGA-Ⅱ)is proposed.Trajectory function is planned with a new composite polynomial that by combining of quintic polynomi-als with cubic Bezier curves.Then,an INSGA-Ⅱ,by introducing three genetic operators:ranking group selection(RGS),direction-based crossover(DBX)and adaptive precision-controllable mutation(APCM),is developed to optimize trav-elling time and torque fluctuation.Inverted generational distance,hypervolume and optimizer overhead are selected to evaluate the convergence,diversity and computational effort of algorithms.The optimal solution is determined via fuzzy comprehensive evaluation to obtain the optimal trajectory.Taking a serial-parallel hybrid manipulator as instance,the velocity and acceleration profiles obtained using this composite polynomial are compared with those obtained using a quintic B-spline method.The effectiveness and practicability of the proposed method are verified by simulation results.This research proposes a trajectory optimization method which can offer a better solution with efficiency and stability for a point-to-point task of robot manipulators.

College of Mechanical Engineering,Zhejiang University of Technology,Hang-zhou 310023,China;Key Laboratory of E&M,Ministry of Education&Zheji-ang Province,Zhejiang University of Technology,Hangzhou 310032,China

[1]Zesheng Wang;Yanbiao Li;Kun Shuai;Wentao Zhu;Bo Chen;Ke Chen-.Multi-objective Trajectory Planning Method)[J].中国机械工程学报,2022(01):70-84

polynomi,APCM,hypervolume

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