【摘要】 针对某打磨机器人的位姿误差分析,提出一种采用二进制和实数值混合编码的遗传BP网络法,同时优化网络结构和权值矢量;网络结构采用二进制编码保留了粒度编码方法的优点,对连接权值系数的实数编码进行Solis&Wets运算使新的遗传算法具有进化规划和进化策略的优点;结合遗传算子和Solis&Wets算子生成后代的方法以及最佳个体保留策略使得遗传搜索空间的群体多样性更好,加快了遗传算法的收敛速度;采用动态参数编码方法替代Vittorio粒度编码方法,既提高了连接权系数的优化精度,又避免了Vittorio粒度变化所引起的适应度的剧烈不连续变化.仿真和实验结果均表明该算法能有效克服遗传算法的非成熟收敛,提高机器人的位姿精度.更多还原

【Abstract】 For the pose error analysis of a polishing robot,on the basis of Vittorio granularity coding method,an improved genetic neural BP network combining binary with real-value coding is proposed with the network architecture and weight vectors optimized.Introducing the binary coding in network architecture to keep the virtues of Vittorio granularity encoding,real-value coding with connecting weighting coefficients,Solis & Wets operation is carried out to bring the virtues of evolutionary programming and evolutionary strategy to the new genetic algorithm.In addition,the combination of genetic algorithm with progeny generated by Solis & Wets operation and optimized individual selection make the genetic search space more diverse to accelerate the genetic algorithm’s convergence,with the dynamic parameter coding used instead of Vittorio granularity coding.Optimization of connecting weighting coefficient will be more accurate,and the sharply discontinuous change of adaptability due to Vittorio granularity change can be avoided.Simulation and experimental results indicate that this algorithm can eliminate genetic algorithm’s premature convergence and improve effectively the robot pose accuracy.更多还原