基于IAGA的工业机器人时间最优轨迹规划

Time Optimal Trajectory Planning for Industrial Robots Based on IAGA

针对工业机器人在给定路径点和运动学约束条件下寻求时间最优解,提出了一种改进的自适应遗传算法(Improved Adaptive Genetic Algorithm简称IAGA)。在改进的算法中设计了一组随种群适应度变化的非线性交叉和变异算子,使算法易跳出局部解,寻求全局最优解能力更强。在关节空间中通过五次非均匀B样条进行插值,以运行时间为优化目标和运动学为约束条件,采用改进的算法进行寻优。寻优结果与其它优化算法相比,结果表明,改进的自适应遗传算法在搜索精度和稳定性均强于其它算法,所得的时间更短且速度、加速度和冲击曲线平滑连续。

An improved adaptive genetic algorithm(IAGA)is proposed for industrial robots seeking the optimal time solution under given path points and kinematic constraints.In the improved algorithm,a set of non-linear crossover and mutation operators that change with the fitness of the population is designed to make the algorithm easy to jump out of the local solution and to find the global optimal solution.Interpolation is performed in joint space by quintic non-uniform B-splines,with the optimization of running time as the optimization target and kinematics as the constraint conditions.Compared with other optimization algorithms,the results of optimization search show that the improved adaptive genetic algorithm is better than other algorithms in search accuracy and stability,and the resulting time is shorter and the speed,acceleration,and jerk curves are smooth and continuous.