为提高机器人的智能性与灵活性, 文章将人的示教引导作用引入到机器人控制系统中来, 通过设计具有适应性的人机交互控制策略, 使机器人能在人的引导下, 智能地配合操作人员完成指定的任务. 这种基于人机交互策略思想所设计的机器人, 克服了早期机器人仅能按照预先设定好的程序来完成指定动作序列的弊端, 拓宽了机器人的使用范围. 为了实现上述目标, 文章设计了一个双臂机器人在操作人员引导下移动物体的实验. 在实验中, 机器人的主动臂直接由操作人员通过力/触觉反馈设备进行控制, 从动臂能够自动调整其运动状态以配合主动臂的运动. 最终, 物体在两臂的夹持下, 按照主动臂的运动轨迹移动. 文章采用了一种基于函数逼近技术(Function Approximation Techniques-FAT) 的自适应阻抗力控制器并将其应用到上述实验中从动臂的运动控制. 在仿真实验中, 通过与PD(比例微分)+前馈补偿控制器的结果比较, 验证了所设计的基于FAT 的自适应控制器具有较好的控制效果.

To make a robot more intelligent and flexible, this paper introduces the human as one element of the higher level loop of robot control system so as to combine the intelligence of human operator and programmed mechanical functions of the robot through a model of human robot interaction. In such a system, the robot system can cooperate with human operator to complete the assigned task, which may overcome the shortcomings of previous robots that mainly aim to repeat the predesigned motion sequence, and may greatly extend the robot's usage in terms of unlimited functions. In order to achieve the above goal, we design a control scheme for the bimanual robot manipulation, in which the leading robot arm is directly manipulated by a human operator through a haptic device and the following robot arm will automatically adjust its motion to match the operator's motion. An adaptive impedance controller based on function approximation techniques (short for FAT) is introduced and applied in the bimanual robot manipulation. Extensive simulation experiments verify that the FAT-based adaptive controller outperforms the proportional derivative (PD) plus feed-forward compensation controller, although they can both complete the specified task by successfully tracking the leading robot arm controlled by the human operator.