基于多点触摸的协作机器人近似最优人机交互控制算法

Approximate Optimal Human-Computer Interaction Control Algorithm for Collaborative Robots Based on Multi-point Touch

针对人机交互系统中现有方法无法准确捕捉用户的操作意图,动态环境适应性较差,导致人机交互精准性不佳的问题,为提高协作机器人在作业过程中的人机交互精准性,提出一种基于多点触摸的近似最优人机交互控制方法.首先,基于人机交互的多点触摸动作匹配,建立交互手势动作序列图像传导函数,提取交互手势特征,分析图像相似度特征分量,根据像素值得出动作判断的模糊度集合,以实现对多点触摸动作的匹配,从而准确捕捉用户的操作意图;其次,考虑机器人运动条件和摩擦力因素,建立摩擦力近似最优约束方程,以保证机器人移动交互的平衡性和稳定性;最后,获取交互手臂的期望响应,利用Lagrange方程描述多点触摸条件下的人机交互状态,建立交互动作动力学方程,引入交互控制变量,利用自适应模糊控制体系输出近似最优控制结果,以提高动态环境适应性,并根据实际情况调整控制策略,更好地满足人机交互的需求.实验结果表明,该方法能有效完成人机交互控制,识别率高达94%以上,控制时延误差较小,为0.03×10^(-3)s,且迭代收敛速度快,具有更好的控制效果.

Aiming at the problem that existing methods in human-computer interaction systems could not accurately capture the user’s operational intentions and had poor adaptability to dynamic environments,resulting in poor accuracy of human-computer interaction.In order to improve the accuracy of human-computer interaction in the operation process of collaborative robots,we proposed a multi-point touch based approximate optimal human-computer interaction control method.Firstly,based on human-computer interaction for mul ti-point touch action matching,we established an image conduction function for interactive gesture action sequences,extracted interactive gesture features,analyzed im age similarity feature components,and obtained the fuzziness set of action judgments based on pixel values to achieve matching of multi-point touch actions a nd accurately capture user operation intentions.Secondly,considering the motion conditions and friction factors of the robot,we established an approximate optimal constraint equation for friction to ensure the balance and stability of the robot’s interaction and movement.Finally,we obtained the expected response of the interactive arm,describ ed the human-computer interaction state under multi-point touch conditions through Lagrange equation,established the interaction action dynamics equation,introduc ed interaction control variables,and used adaptive fuzzy control system to output approximate optimal control results to improve dynamic environment adaptability.We also adju sted control strategies according to actual situations to better meet the needs of human-computer interaction.Experimental results show that the proposed method can effectively achieve human-computer interaction control,with recognition rates of over 94%,and a small delay difference of 0.03×10^(-3) s during control,with fast iteration convergence speed and better control effect.