To optimize the three objectives of efficiency， energy consumption and impacts at the same time， a multi-objective trajectory planning model was proposed based on an improved SSA. Firstly， the artificial potential field method （APF） was used for path planning to obtain the shortest and collision-free path of the manipulator grasping the materials， and the key motion sequence was extracted to establish a multi-objective function. Then， aiming at the problems of multi-objective salp swarm algorithm （MSSA）， such as poor diversity of initial population， easy to fall into local optimum and slow convergence in solution set space， an improved algorithm namely logistic-sine multi-objective salp swarm algorithm（LMSSA）was proposed. The algorithm combined logistic-sine chaotic mapping， pinhole imaging learning strategy and golden sine development strategy to optimize the control nodes of the seventh-order B-spline curve and complete the multi-objective motion trajectory planning of the robotic arms. Finally， the trajectory planning model was applied to the actual grasping tasks of the manipulator UR16e by building MATLAB-CoppeliaSim-UR16e experimental platform. Experimental results show that based on LMSSA， the manipulator motion planning method realizes the accurate， efficient and energy-saving motion trajectory planning of the manipulator， and is successfully applied to the actual operation scenes.
