【作者】 赵亮；

【导师】 程红太；

【作者基本信息】 东北大学 ， 机械工程（专业学位）， 2020， 硕士

【摘要】 装配是将一组零件连接装配在一起的一项任务。一些过程需要推动、冲压、扭曲行为,这可能产生巨大的接触力,而其他过程可能需要精确定位。因此,必须适当地控制位置和力以完成装配任务。基于力的装配解决方案通常针对特定任务和配置而设计。设计、编程和调试过程非常耗时。从演示中学习(LfD)是一种有效的方法,可以隐含地将人类知识转移到机器人中。它依靠演示方法来引导机器人并同时记录传感器数据,从演示过程所记录的数据中学习出技能知识并进行任务的复现。现有的演示技术都有自己的应用场景和局限。基于手势的方法提供了一种不受约束的自然方式来操作机器人。然而,人体运动的不确定性和低分辨率特征以及缺乏力反馈,使得机器人很难完成柔顺和精确的运动。本文提出了一种新颖的基于手势的柔顺装配技能演示系统。通过使用左手作为指挥和右手作为定位,可以即时调整不同的操作模式和缩放比例以满足准确性和效率要求。此外,开发了基于振动的力反馈系统以向操作员提供远程的临场感,来感知装配过程中的接触力。单纯针对某一技能进行演示学习是低效的,并且割裂了其在装配过程中的上下文联系。技能与前后技能相互衔接,隐藏有边界条件和约束条件、技能与环境和任务间同样包含多种约束。相比运动轨迹,这些约束能够更准确反映技能背后的知识。因此,本文基于任务约束提出了一个柔顺装配技能学习框架,研究了柔顺装配过程中的任务分割,控制策略的选择以及技能建模与任务复现的方法。此外,对于任务复现过程中,当环境参数发生变换时,为了保证机器人以及零部件的安全,本文提出了一个机器人连杆避障算法。最后,轴孔装配任务用于测试本文开发的演示系统的有效性,为了验证本文所提出的柔顺装配技能学习方法,本文基于Novint Falcon平台进行了高精度轴孔装配实验,证明了该技能学习算法的有效性。之后对基于手势和基于Novint Falcon两个遥操作平台进行了对比实验,得出本文所提出的基于手势的遥操作系统由于其良好的可扩展性,可以执行次高精度的操作,可以选择合适的工作模式,使次高精度任务更快的完成。最后在基于手势的演示平台上进行了自适应避障实验,验证了本文所提出的机器人连杆避障算法的有效性。更多还原

【Abstract】 Assembly is the task of joining together a group of parts.Some processes require pushing,stamping,and twisting behaviors,which can generate huge contact forces,and other processes may require precise positioning.Therefore,position and force must be properly controlled to complete the assembly task.Force-based assembly solutions are often designed for specific tasks and configurations.The design,programming and debugging process is time consuming.Learning from Demonstration(LfD)is an effective way to implicitly transform human knowledge into robots.It relies on a demonstration method to guide the robot and record sensor data at the same time,learn skills and perform task reproduction from the data recorded during the demonstration.The existing demonstration technologies have their own application scenarios and limitations.Gesture-based methods provide an unconstrained natural way to operate a robot.However,the uncertain and low-resolution features of human motion and the lack of force feedback make it impossible to produce compliant and precise robot motion.This paper presents a novel gesture-based flexible assembly skill demonstration system.By using the left hand as the commander and the right hand as the positioning,you can instantly adjust different operating modes and zoom ratios to meet accuracy and efficiency requirements.In addition,a vibration-based force feedback system was developed to provide the operator with a remote telepresence to sense the contact force during assembly.Demonstrating learning based on a certain skill alone is inefficient and cuts off its contextual connection during assembly.Skills are interconnected with pre-skills and postskills,with hidden boundary conditions and constraints.Skills,environments,and tasks also contain multiple constraints.These constraints can more accurately reflect the knowledge behind the skills than the motion trajectory.Therefore,this paper proposes a compliant assembly skill learning framework based on task constraints,and studies task division,control strategy selection,and skill modeling and task reproduction methods in compliant assembly.In addition,in the process of task reproduction,when the environmental parameters change,in order to ensure the safety of the robot and its components,a robot linkage obstacle avoidance algorithm is proposed in this paper.Finally,the peg-in-hole assembly task was used to test the effectiveness of the demonstration system presented in this paper.In order to verify the flexible assembly skill learning method proposed in this paper,a high-precision peg-in-hole assembly experiment based on Novint Falcon platform was performed,which proved the effectiveness of the algorithm for this skill learning.Later,two experiments were performed on two remote operation platforms based on gestures and Novint Falcon.It is concluded that the gesturebased teleoperation system proposed in this paper can perform sub-high-precision operations due to its good scalability,and can choose a suitable working mode to make the sub-high-precision tasks complete faster.Finally,an adaptive obstacle avoidance experiment is carried out on a gesture-based demonstration platform,which verifies the effectiveness of the robot linkage obstacle avoidance algorithm proposed in this paper.更多还原