基于改进遗传算法的工业机器人能耗最优轨迹规划

为了降低工业机器人在工作过程中的能耗,提出了一种能耗最优的轨迹规划方法。将机器人的轨迹视为由空间中一系列的型值点构成,每相邻的型值点间由一段五次B样条曲线连接,得出机器人的轨迹函数。以动能作为目标能耗函数,同时考虑各个关节的运动学和动力学约束。对遗传算法进行改进,用于优化目标能耗函数,此改进遗传算法提高了算法的运算效率、局部搜索能力和实时性。对优化结果进行仿真,得出各个关节的运动学参数变化曲线,分析各个关节的曲线图知其均满足运动学和动力学约束条件,验证了此优化轨迹的合理性。

ENERGY CONSUMPTION OPTIMAL PLANNING OF INDUSTRIAL ROBOT TRAJECTORIES BASED ON IMPROVED GENETIC ALGORITHM

In order to reduce the energy consumption in the process of industrial robot, a trajectory planning method of energy consumption optimal was proposed. The robot's trajectory was regarded as composed of a series of values point in space, each adjacent value points were connected by a period of five B-spline curves, and the trajectory of robot function was concluded. With kinetic energy as the target function of energy consumption, at the same time the constraints of kinematics and dynamics of each joint were considered. To improve the genetic algorithm for optimizing target function of energy consumption, the improved genetic algorithm improved the operation efficiency, local search ability and real time. The kinematics parameter curves of each joint were derived from the simulation of optimization results, the each joint met the kinematic and dynamic constraints through analysis of the curves of each joint and the rationality of the optimal trajectory was verified.