杨桂林.工业机器人运用技术[J].中国科学院院刊,2015,30(6):785-792.

工业机器人运用技术

Applied Industrial Robotics

杨桂林
中国科学院宁波材料技术与工程研究所 宁波 315201
Yang Guilin
Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China

         工业机器人;运用技术;示教编程;误差补偿;力-运动混合控制

        industrial robot;applied industrial robotics;teaching and programming;error compensation;hybrid force-motion control

        工业机器人运用技术是指在现有工业机器人的基础上所研发的应用技术,其主要目的是增强工业机器人的感知适应能力、降低用户使用难度、缩短示教与编程时间、提高工业机器人的运动精度以及拓宽其应用范围。然而,工业机器人运用技术一直以来没有得到足够的重视,造成工业机器人的设计制造与应用需求脱节,不仅限制了工业机器人的应用和普及,也制约了工业机器人产业自身的大规模发展。文章针对现有工业机器人在制造自动化应用中所存在的问题,系统地归纳和分析了工业机器人运用技术的重点研发方向,主要包括：提高易用性的直觉示教与快捷编程、提高绝对定位精度的运动标定与误差补偿以及拓宽应用范围的力-运动混合控制等关键共性技术。部署和实施相关技术的研发对于提高我国工业机器人的运用水平、加快制造业向智能化升级的步伐具有重要的意义。

        Applied Industrial Robotics(AIR)refers to the application technologies developed on the basis of existing industrial robots. The major research and development objective of AIR is to enable the existing industrial robots with enhanced sensing and adaptability, user-friendly human-machine interface, shortened preparation and deployment time, improved positioning accuracy, and expanded applications. However, AIR has not been received sufficient attention in robotics community, which results in a big technology gap between robot manufacturing and industrial applications. Such a situation not only affects the adoption and application of industrial robots, but also limits the growth of the industrial robot manufacturing industry. Based on the technical problems encountered in utilization of industrial robots for manufacturing automation, this paper introduced the major research and development works as well as future directions pertaining to AIR, such as intuitive teaching and rapid programming(ease of use), kinematic calibration and error compensation(improvement of accuracy), and hybrid force-motion control(expansion of application)technologies. The existing teaching and offline programming methods of industrial robots have the limitations of neither efficient nor accurate, which make the industrial robots difficult to use. As such, three major intuitive teaching and rapid programming approaches have been investigated, i.e., lead through teaching, teaching and programming based on multi-sensor fusion, and programming by demonstration. Current research efforts are focused on intuitive programming with an augmented environment through the fusion of the workpiece CAD model and the sensor information. It is well known that the industrial robots have high repeatability but low absolute accuracy, which makes the off-line programming method inaccurate. A variety of robot calibration and error compensation techniques have been studied, such as the error model based offline calibration, tool-based in-situ calibration, and sensor guided on-line error compensation. Current research efforts are focused on the machine learning method for the calibration and compensation of non-geometric errors due to the robot stiffness, gravity and loading effects so as to future improve the absolute accuracy of industrial robots. Most of the industrial robots are short of force control functionality, which makes them difficult to perform continuous contact-type operations, such as chamfering, deburring, and polishing. To expend the applications of industrial robots from the conventional non-contact type positioning applications to advanced contact type applications, two major force approaches have been investigated, i.e., through the arm approach and around the arm approach. The former refers to the direct force control methods based on the dynamics model of robotic arm which are more suitable for light-weight industrial robots; while the later refers to the force control methods based on add-on force-controlled end-effector modules, which are especially suitable for heavy-duty industrial robots. To achieve safe human-robot interaction, current research efforts are focused on variable stiffness(or impendence)control of robotic manipulators based on their operational-space dynamics and joint-space complaint motion control schemes. With the advances of network communication technologies, sensing technologies, and artificial intelligence, future research and development directions pertaining to AIR will be explored to achieve:(1)enhanced networking features for online remote monitoring, control, and diagnoses;(2)effective multi-robot collaboration through online information sharing, coordination and control;(3)adaptability to semi-structured and unstructured working environments through advanced 3D sensing and intelligent perception techniques;(4) user-friendly human-robot interface based on augmented reality technologies for intuitive simulation, teaching, programming, and control; and (5) modular and task-oriented application programs to shorten the preparation and deployment time. However, most the AIR technologies being investigated are still not ready for industrial applications. Therefore, to initiate major R&D programs pertaining to AIR technologies to advance their technology readiness levels are of great importance, which will not only improve the utilization level of industrial robots, but also speed up the pace of upgrading our nation's manufacturing industries towards intelligent manufacturing.